Data as new resources in the society

• Head: Dr Mika Ala-Korpela, Professor of Computational Medicine
• Faculty: Faculty of Medicine, Research Unit of Population Health, Systems Epidemiology
• Group composition: We are a small and modern research team of two professor-level senior scientists, two postdoctoral specialists, and a handful of other researchers at various academic career stages - we provide a relaxed and enthusiastic working environment though we do aim at high quality research and call for dedication to science
• Website: https://www.oulu.fi/en/node/25592
• Contact: mika.ala-korpela (at) oulu.fi

Epidemiology is the cornerstone of public health that seeks to understand the patterns of diseases in human populations and the causal risk factors that can be targeted in order to prevent those diseases. We are globally recognized pioneers in the subfield of epidemiology that is focused on data-intensive investigations of blood and urinary molecules. Our mission is to determine predictive and potentially causal factors for common age-associated conditions such as obesity, the metabolic syndrome, diabetes and its complications, and heart disease.

We are looking to expand our international recruitment for a new systems epidemiology program. The over-arching aim of the program is to identify synergistic molecular drivers that worsen known disease risk factors, influence drug metabolism or increase the risk of adverse clinical events directly. The emphasis will be on the compounding of multiple risk factors (systems perspective) that, over time, lead to cardiovascular disease or type 2 diabetes late in life. This is different from the traditional approach that aims to isolate the impact of a single risk factor in the absence of others. That said, we will continue to also rely heavily on the classical epidemiological approaches as they are easier and less risky. To achieve the program aims, we are already developing novel metabolomics technology that will provide new large-scale datasets from existing urine samples. We have also the capacity to apply and develop powerful statistical methods that are essential to detect the most probable risk drivers from biochemical, genomics and clinical records. In total, the collection of resources within our collaborative network in different countries includes over 800,000 individuals and we are searching for talented bioinformaticians and research software creators to facilitate the transformation of the raw data into impactful epidemiological knowledge.

We encourage potential candidates to contact us directly, and we expect postdoc researchers to create a pool of research interests and ideas that they are keen to pursue and that we can discuss, filter and develop into specific high-quality projects that fit within the framework of the research program. We value inter-discplinary individuals who can harness the power of the computer and who can simplify complex biomedical findings into understandable yet statistically robust presentations that have utility as public health evidence or that provide novel biological insight into cardiometabolic disease etiology. Our senior investigator team includes the program lead (Prof Ala-Korpela), specialised in metabolomics & systems epidemiology, the statistical & technical development lead (Dr Mäkinen) specialised in bioinformatics, machine learning & AI, and genomics and an experimental lead (Dr Tynkkynen) who is specialised in biosample handling, analytical chemistry, and NMR spectroscopy (i.e. the backbone of our metabolomics platforms). Our local collaborators include some top geneticists in Finland and we have personal collaborative links with leading research groups in the UK, Spain, Australia and multiple other countries across the world.

Key words: Machine learning and AI, metabolomics, systems epidemiology, ageing & cardiometabolic diseases

• Head: Zhi Jane Chen, Associate Professor
• Faculty: Biochemistry and Molecular Medicine
• Group composition: 1 Associate Professor, 1 postdoctoral fellow, 3 doctoral researchers, 1 master student
• Website: https://www.oulu.fi/en/university/faculties-and-units/faculty-biochemistry-and-molecular-medicine/disease-networks
• Contact: zhi.chen (at) oulu.fi

Inflammation and cancer are worldwide most common and life-threatening diseases. The social and financial burdens by these chroInic, debilitating diseases include poor quality of life, high health care costs, and substantial loss of productivity and death. Better molecular understanding and new targeted therapies are urgently needed.

The immune system plays an essential role in these disorders. T cells orchestrate immune responses to a variety of pathogens. In addition, they also play a critical role in inflammation, autoimmunity, and tumor. In the "Lymphocytes and inflammation" group, we study T cell functions at multiple levels from mouse and human and integrate the results to build a comprehensive view of T cell subsets functions in inflammation and tumorigenesis. Utilizing recently established state-of-the-art OMICS approaches, such as transcriptomics including single-cell transcriptomics and spatial transcriptomics, epigenomics and proteomics from cancer patients' samples obtained from biobanks, mouse disease models as well as from vitro cellular models, large amounts of datasets have been and will be generated. These data provide valuable resources to better understand the molecular and cellular mechanisms of T cell functions in development of inflammation and tumorigenesis. Generation, deeper exploration, integration and mining of multi-OMICS data will facilitate ongoing efforts to identify molecules or pathways critical for immune responses in inflammation and tomorigenesis. Characterization of functions of these molecules by in vitro assays and established in vivo disease models may lead to development of novel therapies to treat inflammatory diseases and cancer.

As one of the Biocenter Oulu Spearhead projects, the project will be carried out at the Faculty of Biochemistry and Molecular Medicine, University of Oulu. The animal core facility closely linked to this project as well as Biocenter Oulu core facilities such as FACS core, imaging core, proteomic core and transgenic core as well as biobank are conveniently located in the campus. The group is part of Disease Networks research unit, which is composed of multidisciplinary research groups. Our well established national and international collaborators from the Europe, U.S.A and China with extensive expertise on in vivo disease models, T cell biology, omics technologies and bioinformatics will ensure the proposed project can be successfully carried out.

Key words: immunology, omics

• Head: Vahid Farrahi, Assistant professor
• Co-PI: Mikko Kärmeniemi, PhD
• Faculty: Faculty of Medicine
• Group composition: 2 professors, 5 postdocs, 6 researchers
• Research group website: https://www.oulu.fi/en/research-groups/wearables-for-population-health
• Contact: vahid.farrahi (at) oulu.fi

Today’s wearable devices—such as smartwatches, smart rings, armwear, and activity and fitness trackers—typically come equipped with multiple sensors. Depending on the sensors incorporated, wearable devices can objectively and continuously record and track various types of physiological and behavioral data. These moment-by-moment quantification of physiological and behavioral data collected as the users live their ‘everyday life’ could encapsulate clinically- and medically-relevant information. With the use of advanced analytical tools and modeling techniques such as machine learning and artificial intelligence (ML/AI), these relevant information can be captured and transformed into actionable insights for health promotion and healthcare.

Applying ML/AI to the vast quantities of wearable data creates opportunities for health monitoring, early detection of risks, and personalized interventions. While much of the current research on wearable technology focuses on providing real-time feedback to users, wearable data also hold significant promise for prevention of major non-communicable diseases and enhancing healthcare practices by transforming raw sensor data into predictive and diagnostic tools.

Our central hypothesis is that data from wearable health technologies can inform diagnostic, prognostic, and risk assessment models for major non-communicable diseases. In particular, conditions such as cardiovascular diseases, depression, dementia, Parkinson’s disease, and Alzheimer’s disease may be managed more effectively, or even anticipated, through wearable-driven predictive models. Large-scale analysis of wearable data can reveal patterns and indicators that are clinically relevant; for instance, changes in physical activity, sleep quality, or movement patterns might signal early signs of diseases associated with lifestyle or age-related conditions.

Our objective is to utilize wearable data from the Northern Finland Birth Cohorts (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/…)—unique, population-based cohorts with lifelong health data—to develop digital biomarkers that can predict and monitor future health conditions. Using innovative data-driven, ML/AI techniques, we aim to develop and validate models that support the prevention, detection, and management of various health conditions. Our multidisciplinary research group has extensive expertise in working with wearable data, ML/AI modeling, and large-scale population-based studies.

Key words: Wearable health, Digital biomarker, Health monitoring, Behavior tracking

• Head: Mikko Finnilä, Academy Research Fellow
• Faculty: Faculty of Medicine
• Group composition: Principal investigators (senior researchers): 2, postdocs: 3, PhD students: 8
• Website: https://www.oulu.fi/en/researchers/mikko-finnila
• Contact: mikko.finnila (at) oulu.fi

Immune cells control and modulate various biological process. This topic aims to explore immune cell profiles in joint diseases and colorectal cancer and link those to angiogenesis, innervation and extracellular matrix remodeling. We have selected joint diseases and colorectal cancer as disease areas based on clinical expertise of research group supported with biomedical imaging and machine learning expertise. The project is expected to improve prognostics in clinical pathology and reveal novel targets for drug development and improve implant survival.

More specifically we aim to understand origins of pain by studying immunological landscape in joint tissues in relation to innervation and matrix modelling; biological mechanisms of immune cell responses to prosthetic wear-tear particles resulting in implant failures; and immunological cell profiles in colorectal cancer in respect to patient survival.

To tackle these complex issues, we will use multimodal imaging approach including immunohistochemical multiplexing, contrast enhanced x-ray microtomography and multiphoton microscopy. Utilizing this big data will require establishing fluent dataflows and storage solutions integrated with high-performance computing for segmentations with neural networks and other artificial intelligence methods to enable complex morphometric analysis. Finally, we will further develop data visualization tools to present acquired results.

Key Words: Multimodal microscopy, pathology

• Head: Reetta Hinttala, Professor of Molecular and Cell Biology in Medicine
• Faculty: Research Unit of Clinical Medicine, Faculty of Medicine, and Medical Research Center, Oulu University Hospital and University of Oulu; Transgenic and Tissue Phenotyping Core Facility, Finnish Node of Infrafrontier (ESFRI), Biocenter Oulu, University of Oulu
• Group composition: 1 professor, 4 post docs, 2 PhD students, 2 laboratory technicians
• Websites: https://www.oulu.fi/en/research-groups/pediatric-neurology-group
• https://www.oulu.fi/en/university/faculties-and-units/biocenter-oulu/transgenic-and-tissue-phenotyping-core-facility
• Contact: reetta.hinttala (at) oulu.fi

Brain disorders belong to major public health diseases causing high numbers of morbidity, mortality and impaired quality of life addressing the importance of neuroscience research and its translation into novel diagnostic and treatment strategies. Our research project in the Data4Healthcare programme is focused on early-onset neurodevelopmental and -degenerative diseases caused by novel or Finland-enriched genetic variants. The goal is to reveal pathways of genes and protein networks causative for FINCA disease, Rett syndrome, Northern epilepsy and Hydroletalus syndrome. Investigations will form the basis for development of early detection markers, better diagnostics, and treatment options for these devastating diseases. The project combines functional molecular and multiomics data including transcriptomics, proteomics and metabolomics from cell culture and mouse models, taking advantage of state-of-the art biomedical technologies through collaboration with European centers specializing in mouse genetics and phenotypic analysis.

Our recent findings on NHLRC2, CLN8 and HYLS1 create a framework for further clarification of their role in human health and disease. The short-term goal of the project is to reveal the interacting protein networks during neurodevelopment and -degeneration using techniques such as in utero electroporation and proximity labelling by biotin ligase (BioID2). In the midterm, the view is extended to the physiological pathways at the organism level using knock in mouse models recently created with CRISPR/Cas9 technique by the Hinttala lab. Machine learning-based data analysis is used to characterize and translate histopathological findings of disease models to humans. The long-term goal is to understand the pathophysiology behind studied diseases in order to identify solutions on how to control reactions leading to neurodevelopmental disorders and/or neurodegeneration.

Hinttala lab has a solid background in identification of novel disease genes leading to neurological disorders as well as in analysis of in vivo and in vitro disease models in collaboration with clinical researchers from Oulu University Hospital. The research laboratory is located at the Clinical Research Center (CRC) and maintained by Oulu University Hospital and University of Oulu. CRC and the neighboring Biocenter Oulu (BCO) form a complex of well-equipped laboratories for tissue-culture and molecular biology experiments. The BCO provides a wide range of high scientific quality core facilities, including Transgenic and Tissue Phenotyping core facility coordinated by Prof. Hinttala and Tissue Imaging Center for ultrastructural analysis of tissue samples and in vitro/vivo models. The Laboratory Animal Center in Oulu provides laboratory animal services including animals and facilities.

All in all, we offer a stimulating environment for a postdoc researcher who is interested in exploring novel molecular mechanisms behind neurological diseases. Ultimately, our research will provide novel knowledge for prevention of severe disability and improvement for diagnostics of diseases associated with central nervous system development and degeneration in children. By influencing prenatal diagnostics, genetic counselling, treatment options and long-term prognosis, this project aims to have a significant impact on the quality of life for both affected individuals and their families.

Key words: brain, pediatric neurology, disease modelling

• Head: Janne Hukkanen, Professor of Internal Medicine (University of Oulu), Chief Physician (Oulu University Hospital)
• Co-PI: Jukka Hakkola, Professor of Pharmacology
• Faculty: Faculty of Medicine, Research Unit of Biomedicine and Internal Medicine
• Group composition: Research group of PI Hukkanen: 1 professor, 1 professor emeritus, 2 post-doc researchers, 4 PhD students as well as 2 research dieticians and 2 study nurses, and a laboratory nurse; Research group of co-PI Hakkola: 1 professor, 2 post docs, 3 PhD students
• Websites: https://www.oulu.fi/en/researchers/janne-hukkanen; https://www.oulu.fi/university/researcher/jukka-hakkola
• Contact: janne.hukkanen (at) oulu.fi

Obesity, type 2 diabetes, metabolic dysfunction-associated steatotic liver disease, and other metabolic diseases have reached epidemic proportions and constitute a major public health concern worldwide. While excess caloric intake and sedentary lifestyle are well-accepted major risk factors for metabolic disorders, also environmental chemical exposure has negative impact on metabolic health, among which endocrine disrupting chemicals (EDCs) are the most well-established.

A major mechanism mediating the harmful metabolic effects of EDCs is activation of xenobiotic-sensing nuclear receptors. We have previously uncovered novel detrimental effects of xenobiotic-sensing by pregnane X receptor (PXR) on glucose and cholesterol metabolism, endocrine function, and blood pressure regulation. Crucially, we have also discovered a previously unexplored circuit combating the harmful effects of EDC as a PXR-regulated metabolite of cholesterol, 4β-hydroxycholesterol, lowers blood pressure and enhances the reverse cholesterol transport. We now aim to uncover potential of utilizing beneficial effect of physiological down-stream effects of xenobiotic-sensing nuclear receptors for treatment and prevention of metabolic diseases. The ultimate goal is to develop ways to augment the beneficial pathways, lessen the effects of harmful chemicals and protect the public health.

In our clinical research unit, we perform trials with healthy volunteers and patients to discover new effects of endocrine disruption and find ways to enhance the function of the body’s own physiological circuits to combat the harmful EDC effects. In our preclinical laboratory, we utilize mouse and cell models to investigate in detail the molecular mechanisms of metabolic disruption upon exposure to EDCs. We are part of an EU funded consortia that brings together experts of toxicology, medicine, epidemiology and systems biology to address the challenge of endocrine disrupting chemicals and metabolic diseases. In this multidisciplinary network, together with our partners, we combine our various experimental models and human cohort data and samples and produce multiomics data including RNA-sequencing, ATAC-sequencing, metabolomics and proteomics and integrate the data with bioinformatics and systems biology approaches.

A successful applicant should be interested in the designing and performing clinical trials and combining experimental biomedicine approaches with big data analysis.

Key words: Therapeutics, multiomics, xenobiotic-sensing, PXR, 4β-hydroxycholesterol

• Head: Valerio Izzi, PhD, Associate professor
• Faculty: Faculty of Biochemistry and Molecular Medicine, ECM-Hypoxia Research Unit
• Group composition: 1 Professor, 1 post doc, 3 PhD students, 2 MD-PhD students
• Websites: https://www.oulu.fi/en/researchers/valerio-izzi; https://www.oulu.fi/en/university/faculties-and-units/faculty-biochemistry-and-molecular-medicine/ecm-and-hypoxia
• Contact: valerio.izzi (at) oulu.fi

The Izzi group is a leading developer of new bioinformatics tools designed to study extracellular matrix (ECM) proteins in tissues and cells. The group’s goal is to gain a comprehensive understanding of how functional interactions between cells and their non-cellular microenvironment, the ECM, control cancer initiation, progression, and metastasis, and how the ECM influences cancer therapy response and resistance. This research aims to enable clinical advancements which can enhance patient survival rates and improve diagnostic accuracy. The project will focus on cancers with limited therapeutic options, aiming to develop interventions with direct patient benefits and to bridge current gaps in treatment. Using multidimensional open data (single-cell, spatial and bulk RNAseq, genomics, proteomics, multiplex tissue profiling, mass cytometry, etc.) and proprietary signaling network analysis tools, the team aims to identify novel diagnostic and prognostic biomarkers in the tumor ECM. More importantly, the team seeks to pinpoint actionable cell surface/intracellular targets that mediate signaling from the tumor microenvironment, with the potential to translate these findings into effective targeted cancer therapies. Success indicators include development of high-precision models to predict protein-protein interactions (PPI), the identification of novel cancer biomarkers and the validation (in vitro and in vivo) of the most promising targets. Additionally, the group specializes in evolutionary/co-evolutionary analyses of the ECM, integrating these with network analysis to uncover both known and predicted interactions. These predicted interactions, central to this project, will undergo deep learning-driven predictions for protein structures and PPIs to identify the most promising and tractable ECM interactions in silico. The final targets, validated as the most promising through in silico approaches, will be further confirmed by determining the structures of protein/protein complexes via cryogenic electron microscopy and/or crystallography and assessing their functional roles with dedicated in vitro and in vivo models. This process aims to fast-track the most impactful ECM targets for clinical application, potentially resulting in groundbreaking tools for cancer treatment.

Key words: cancer, tumor microenvironment, extracellular matrix, therapeutic target, protein-protein interactions, biomarker, AI, machine/deep learning

• Head: Jouni J.K. Jaakkola, MD, DMedSc, PhD (Epid & Biostat), Professor of Public Health, University of Oulu, and Research Professor, Finnish Meteorological Institute
• Faculty: Faculty of Medicine
• Group composition: Professors: 3, University lecturers/researchers: 3, Post-doctoral researchers: 5, Doctoral researcheres: 5, Project researchers: 2 , Lead assistant: 1
• Website: https://www.oulu.fi/en/research-groups/cerh
• Contact: jouni.jaakkola (at) oulu.fi

The mission of the Center for Environmental and Respiratory Health Research (CERH) is to produce and summarize big data for evidence-based promotion of human health and sustainable solutions to factors that threaten the well-being of mankind and ecosystems in a changing global environment. CERH carries out this mission by assessing the health impact and burden of disease from environmental exposures directly or indirectly related to global environmental change mainly driven by climate change and loss of biodiversity.

CERH has since its establishment in 2009 pursued a research program 'Global change, environment and public health' which has produced over 200 publications, trained 10 doctoral students and mentored 10 post-doctoral researchers. The program elaborates the complex causal web from global change, driven by climate change and loss of biodiversity via weather, air pollution, housing, behavior and life style, to respiratory, cardiovascular, reproductive and other diseases of major public health importance. CERH also studies vulnerability of susceptible groups to environmental exposures as well as potential co-benefits related to climate change mitigation. The CERH is multidisciplinary and multicultural team with expertise in planetary health, public health, epidemiology and biostatistics, clinical medicine, biology, ecology, genetics, and anthropology.

CERH applies modern epidemiological methods to several on-going population-based cohort and case-control studies such as the Espoo Cohort Study, The Finnish Environment and Asthma Study and School Environment, The Northern Finnish Asthma Study, and Health Stusy (InChilHealth), national and international registries, and participates in the Multi-Country, Multi-City Collaborative Network with members and data on 120 million deaths from over 800 locations in 49 countries in all continents.

CERH collaborates closely with the Finnish Meteorological Institute, which is one of the leading research institutes in its field in the world and has a capacity to model weather and air quality from local to global level as well as to make future predictions. This data has been transformed to individual-level exposure assessment covering a period starting from 1980's.

The CERH research program and its environmental and health data offer opportunities for a broad scale of research on the general theme of planetary and environmental health. The program offers solutions to support sustainable and well-being promoting choices of people operating at different levels and sectors. The research program offers postdocs an excellent opportunity to study and find solutions to major trends that threaten humankind. Specific research themes include studies on the effects of air pollution, temperature, land cover/use and housing factors on respiratory infections and chronic diseases, cardiovascular diseases, and reproductive health, including application of genetic methods to address gene-environment interaction in asthma to investigate mechanisms conveying susceptibility. One important theme is to study the effects of extreme temperatures in combination with indoor and other outdoor exposures on symptoms, morbidity and mortality from the most common chronic diseases. The CERH research program has extensive European-wide and global research collaboration and exchange of researchers and students, which offers opportunities for fruitful research. We offer opportunities for several focused projects. Please, ask for details.

Key words: planetary health, public health, environmental epidemiology

• Head: Soile Jokipii-Lukkari, Academy Research Fellow, Doc.
• Faculty: Faculty of Sciences
• Group composition: 2 professors and 2 Docents
• Website: https://www.oulu.fi/en/researchers/soile-jokipii-lukkari
• Contact: soile.jokipii-lukkari (at) oulu.fi

Human resilience to disease burden is regulated importantly by the innate and acquired immunity systems. The molecular machinery in the innate immunity is integrated to every cell of the body and it becomes trained as the self in part during embryogenesis and there after via proteolytic processing and subsequent tissue transplantation (HLA) based display of the self-peptides on cell surfaces. By interacting with the innate immunity, the acquired immunity becomes also activated upon exposure to foreign, non-self-invader’s such as those delivered by bacterial, viral and other microbiome derived secreted components represented in these organisms.

The pro- or antibiotic factors such as bacteria, yeast, viruses and parasites impact to human body is connected to secretion of complex molecular secretory load. This is in general represented as the membrane circulated pico-, nano- and micro size vesicles that are humorally presented to the immune system. A fundamental property of these nanosized cell secreted bodies is that they transfer a complex array of molecular cargo via the body fluids. Moreover, these vesicles display enzymes by which they can become transmitted via transcytoses across most if not all biological barriers, such as the gut and enter the blood circulation.

Given the universal nature of the cell secreted nanovesicles and the fact that they cargo wealth of molecules such as DNA, RNA, proteins and metabolites these universal in nature widely acting interspecies signalosomes provide novel means to identify the degree of exposure to a given environment. Thus, the microbiome and food/nutrition nano exposomics impacting human health can be defined based on the presence of DNA, RNA and proteins in nanovesicles. The Finnish Biobank law enables to take use of the GWAS and patient registry data whose usage can be extended further by an ethical licence process. Moreover, the biobank act offers recontacting of those subjects that have given their consent to this and is managed by the Finnish Biobank cluster Fingenious. This enables data confirmation and prognostic studies to be conducted.

In this EU COFUND Data4health post-doctoral project, we will target the working hypothesis that the lower the nature derived species load of the depicted nanocomponent / microbiome is the higher is the disease risk. We speculate that the loss of microbiome species diversity is in correlation to the deterioration or severe reduction of species secretome signalosome via the nanovesicle exosomes in an environmentally dependent manner. Since most if not all species are by known to secrete molecularly loaded, nanostructures that can invade the human body by the mechanism of transcytosis this knowledge may offer means to define via OMICS and genome (DNA, RNA) based data-analysis a novel score for disease risks. The score is expected to define to what degree a given individual has been exposed to environmental nature derived elements. Such a score will be derived from the excretory fluid molecular data including stool, saliva, tears, sweat and urine. These obtained data will be correlated to the Finnish national health registry data to identify the disease burden the analysed subjects.

Key words: DNA Bar coding, meta barcoding, planetary / one health, microbiome, viriome, biocommunication, bioaerosols, soil, water, land, species secretome, biohologram, bioradar, naturescore

• Head: Juhani Junttila, Professor and Risto Kerkelä, Professor
• Faculty of Medicine
• Group composition: Two professors, 2 senior scientists, 5 postdocs, 15 doctoral students
• Websites: https://www.oulu.fi/en/projects/genetic-causes-and-molecular-mechanisms-myocardial-fibrosis
• https://www.oulu.fi/en/research-groups/cardiology-research-group
• Contact: risto.kerkela (at) oulu.fi, juhani.junttila (at) oulu.fi

Cardiovascular disease is the leading cause of worldwide mortality and most cardiac pathophysiological conditions are associated with myocardial fibrosis. The degree of cardiac fibrosis predicts adverse outcome, which may reflect compromised systolic or diastolic ventricular function, or the disruption of normal electrical cell-to-cell coupling and impulse conduction predisposing to arrhythmias and sudden cardiac death. Our aim is to study cardiac samples of series of victims of sudden cardiac death (SCD) to identify genetic factors and molecular mechanisms regulating the development of fibrosis and accumulation of adipose tissue in the myocardium.

We are seeking a highly motivated Postdoctoral Researcher to join our team for a project focused on understanding the biology of heart-related sudden death. The project includes analysis of exome, cardiac transcriptome and clinical data of sudden cardiac death victims.

We have collected a consecutive series of victims of SCD undergoing medico-legal autopsy in the FinGesture study since 1998 until now (N=5869). Our aim is to study cardiac samples of these victims to identify both genetic factors and transcriptional level changes associated with development of cardiac fibrosis and accumulation of adipose tissue in the heart. Cardiac tissue samples of traffic accident victims with no history or evidence of cardiovascular disease at autopsy will serve as controls for the study.

The DNA collected from FFPE (Formalin-Fixed Paraffin-Embedded) tissue sections is used for exome sequencing to examine genetic variations: single nucleotide variants and indels as well as copy number variants. The collected FFPE tissue is also used for 3'mRNA sequencing to study differential gene expression at tissue level and for spatial sequencing that allows to examine local RNA-level changes in cardiac tissue. Frozen cardiac samples, also collected during the study, enable single-nuclei RNA sequencing to provide insight on the disease progression at cellular level.

Candidates with interest in cardiovascular research are encouraged to apply. This position provides an opportunity to gain expertise in genomics and transcriptomics, and contribute to research in understanding the molecular basis of sudden cardiac death.

Key words: sudden cardiac death, exome sequencing, single-nucleus RNA sequencing

• Head: Minna Karjalainen, Adjunct Professor
• Faculty: Faculty of Medicine, Research Unit of Population Health
• Group composition: 1 professor, 2 adjunct professors, 2 PostDocs, 8 PhD students, 4 master’s students
• Website: https://www.oulu.fi/en/researchers/minna-karjalainen
• Contact: minna.k.karjalainen (at) oulu.fi

Our research focuses on combining multiomics datasets to characterize the genetic and molecular mechanisms behind cardiometabolic diseases. In our studies, we utilize large-scale omics datasets from biobanks and large population cohorts, aiming to identify human metabolism and cardiometabolic disease-associated genetic factors and biological pathways.

We conduct multiomics studies to determine genetic factors associated with metabolism, particularly circulating lipids, utilizing data from high-throughput serum nuclear magnetic resonance spectroscopy (NMR) metabolomics and plasma mass spectrometry lipidomics. We also investigate genetics of cardiometabolic diseases, such as metabolic dysfunction-associated steatotic liver disease/non-alcoholic fatty liver disease (MASLD/NAFLD) and coronary atherosclerosis, utilizing genome-wide association studies (GWAS). Our final aim is to characterize biological mechanisms leading to disease, finally allowing identification of novel biomarkers and drug targets, thereby providing novel means for disease prediction and prevention.

As an example, we recently published the largest GWAS of human serum NMR metabolomics (Karjalainen et al. 2024, Nature 628;130–138; https://www.nature.com/articles/s41586-024-07148-y). This study included 136,000 participants from 33 cohorts and 223 metabolic traits. Next, we are conducting the largest lipidomics GWAS to date, utilizing data from over 17,000 individuals from seven cohorts to investigate genetic associations of over 800 individual lipid species. In the disease studies, we utilize genetic data from large biobanks, such as FinnGen, Estonian Biobank and UK Biobank, sample sizes typically reaching hundreds of thousands of individuals. By further combining data from the metabolic platforms to disease studies, we obtain detailed insights into the roles of metabolic processes in disease risk.

The projects for a Postdoctoral researcher could include genetics of human metabolism utilizing high-throughput metabolomics and lipidomics platforms, and/or genetics (GWAS) of cardiometabolic diseases, such as metabolic dysfunction-associated steatotic liver disease/non-alcoholic fatty liver disease (MASLD/NAFLD), to characterize the underlying genetic and biological mechanisms. Please do not hesitate to contact us directly for further insights into potential projects. Our approaches represent high level research and hold a high potential for significant novel discoveries.

Key words: genetics; metabolomics; lipidomics; genetic epidemiology; cardiometabolic disease

• Head: Professor Peppi Karppinen (Koivunen), MD, PhD
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: 1 professor, 5 post docs, 6 doctoral students, 2 technicians
• Website: https://www.oulu.fi/en/research-groups/prof-peppi-karppinen-nee-koivunen
• Contact: peppi.karppinen (at) oulu.fi

The Karppinen research group is an international translational research group focusing on oxygen sensing enzymes as novel therapeutic targets in diseases. We have diverse expertise from biomedicine to enzymology, preclinical disease models, epidemiology and clinical medicine. Being partners in the GeneCellNano Flagship and having experience from long term industry collaboration, we are poised towards innovations.

We are harnessing big data in health care to reach our goals:

1. Establish causality of oxidative enzyme-catalysed post-translational modifications (PTMs) in diseases

2. Assign novel enzyme-catalysed oxidative modifications

3. Identify yet unknown connections between oxygenation and diseases using hemoglobin (Hb) levels as a surrogate marker

By analysing publicly deposited proteomics datasets on patient samples and cohort datasets, and by generating our own proteomics data to identify PTMs in plasma, urine, and FFPE samples from Finnish biobanks, we will build a new data repository focused on oxidative modifications and interrogate their association with diseases. Our longstanding experience on the superfamily of 2-oxoglutarate-dependent dioxygenases (2OGDDs) and the analysis of oxidative PTMs will be crucial in making such assignments that will be of great benefit for the diagnosis and treatment of patients suffering from the diseases.

Deficiency in oxygen, hypoxia, complicates most diseases. Hb is the main carrier of oxygen and its levels directly associate with tissue oxygenation. Hb levels are regulated genetically and environmentally, however, individuals’ Hb levels during adult life are very stable. Among 2OGDDs, HIF-P4Hs act as major cellular oxygen sensors. Under hypoxia inhibition of their activity stabilizes hypoxia-inducible factor (HIF) and initiates a large transcriptional response including reprogramming of energy metabolism. We have shown earlier that within normal variation range, lower Hb levels are associated with leaner body composition, healthier metabolism and reduced risk for cardiovascular disease-related mortality; the lower Hb levels being associated with activated HIF response. This was surprising as in general high Hb levels have been considered beneficial for health. Since the HIF response includes also other components to metabolism, such as induction of angiogenesis and erythropoiesis and regulation of inflammatory and immunological processes, cell cycle and extracellular matrix, we hypothesise that Hb levels are associated with other conditions. Using big data from existing longitudinal cohorts (Terveys 2000, Finnriski, total n = 40,000, follow-up > 20 yrs), we will study which diagnoses (ICD codes) and medications (ATC codes) Hb levels can predict in a population level to enable early intervention strategies.

Key words: Hemoglobin, Hypoxia, Post-translational modifications

• Head: Johannes Kettunen, Professor
• Faculty: Faculty of Medicine
• Group composition: 1 professor, 2 adjunct professors, two PostDocs, 8 PhD students, 2 masters students
• Website: https://www.oulu.fi/en/researchers/johannes-kettunen
• Contact: johannes.kettunen (at) oulu.fi

Our research group focuses on large-scale genetic epidemiology to elucidate the molecular mechanisms of morbidities. We use large biobank studies in genetic epidemiology of various diseases. Our main resource in the FINNGEN project, that we utilize and collaborate with other large biobanks (UKBB, Million veterans program, Estonian biobank) to reach typical samples sizes of over a million participants for given outcome.

Our clinical fields of interest include, but are not limited to, dermatology, ophthalmology, infectious diseases, musculoskeletal diseases, women’s health, pulmonology, and cardiology. For example, our recent two GWAS publications for spinal stenosis and for lumbar disc herniation included more than 700000 participants (stenosis: https://www.medrxiv.org/content/10.1101/2024.10.16.24315641v1.full.pdf and lumbar disc herniation: https://www.nature.com/articles/s41467-024-53467-z) revealing a plethora of new genetic variants and new biology underlying disease risk. The FINNGEN offers outstanding opportunities through complete linkage of individuals through electronic health records. The projects for a PostDoc applicant could include genetic epidemiology using large biobank datasets to unravel novel biological mechanisms underlying common and rare morbidities and study genetic determinants of use of pharmaceuticals (one of our ongoing projects is the use of antimicrobials as a proxy for infection susceptibility). The proposed projects are carried together with The Finnish Institute for Health and Welfare (THL) and the Oulu University Hospital, where secondments are possible. Please note, we do not do wet lab, only computational analysis. The research material and data at hand are at the state-of-the-art, and possibilities are numerous for top class international research projects.   

Key words: Genetics, biobanks, electronic health records

• Head: Thomas Kietzmann, Professor
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: 1 prof, 2 Post-Docs, 2 trainees
• Websites: https://www.oulu.fi/en/bco-projects-2024-2027
• https://www.oulu.fi/en/university/faculties-and-units/faculty-biochemistry-and-molecular-medicine/ecm-and-hypoxia
• Contact: thomas.kietzmann (at) oulu.fi

Our research group, dedicated to exploring the role of hypoxia in obesity and fibrosis with emphasis on hypoxia-inducible factor-independent aspects operates at the forefront of healthcare innovation. Comprising a diverse team of experts, including biochemists, molecular biologists, technology specialists, clinicians, and epidemiologists, our interdisciplinary approach focuses on the dynamic interplay of hypoxia and redox signaling in healthcare.

Our commitment is to revolutionize healthcare through the strategic application of big data analytics and cutting-edge technology. We strive to position data as a new societal resource, utilizing state-of-the-art techniques. By integrating omics technology, computational medicine, and systems biology, we aim to understand hypoxia signaling, metabolism, obesity, and fibrosis across various model systems and patient scenarios. Therein, our group develops predictive models that forecast disease trends, identify at-risk scenarios, and enable early intervention strategies.

In pursuit of these goals, we are actively building comprehensive and accessible marker repositories, providing valuable resources for diagnosis and therapy decision-making. By placing hypoxia and redox signaling at the center of our innovative healthcare solutions, we contribute to a proactive and effective allocation of resources by healthcare authorities.

Key words: hypoxia, obesity, fibrosis, large datasets, metabolic profiling, transcriptomics, proteomics, interactome

• Head: Vesa Kiviniemi, professor of Functional NeuroImaging
• Faculty: Faculty of Medicine
• Group composition: Oulu Functional NeuroImaging (OFNI): 1 professor, 2 post-docs, 15 PhD-students, 1 res. coordinator, 1 res.nurse, 1 data analysist
• Website: www.oulu.fi/ofni
• Contact: vesa.kiviniemi (at) oulu.fi

Several major brain diseases (Alzheimer's disease, stroke, trauma, epilepsy,..) cause alterations in the glymphatic brain mechanisms convecting brain solutes and metabolites by physiological pulsations. Recent advances in ultrafast functional brain imaging with MREG sequence, enables precise 3D detection of the changes in these glymphatic brain pulsations enabling for the first time individual diagnostic accuracy.

This technology could be used in most centers with modern MRI scanners but the shere amount of data especially in raw image format and it’s reconstruction hampers the use of the technology. Another key issue is that the sequence usability for most vendors is still lacking. One key problem is the shere amount of data: a 5 min scan provides 3000 whole brain images with 643 voxels and raw image size is 32-64 times that depending on the parallel imaging coil used.

The aim of this project is to develop a national network for reconstructing large data over National Finnish CSC computing services for wide usage. Then using this resource we sill further enable the preprocessing and analysis of the data over the same network with installed control data for analytics. An internal aim is to collect data for 1000 control brains over normal human adult life spane that enables production of normative age & gender limits for individual diagnostics in MNI coordinate space.

The GDPR-suitable software surface for data sharing has already been established with Aalto University prof. Lauri Parkkonen. Currently we are collaborating with UEF, Aalto, Oulu, Turku and Helsinki Universities, Jyväskylä and Tampere are expected to join after initial steps thus providing data from at least 8 national 3T MRI sites. Also Jena and Freiburg Universities are already within the collaboration with data sharing and analytics. Several US and EU centers are expected to join in later on after the data sharing and vendors issues are solved.

A secondary aim is to provide a vendor agnostic MREG sequence covering most MRI vendors with PulSeq tool. Both MREG and PulSeq are developed in Freiburg University by our collaborator Maxim Zaitsev and Jurgen Hennig. There the MREG vendor free version has been coded to be 95% ready and needs final steps to proceed.

We are searching for a post-doc researcher to develop a national network of ultrafast brain imaging with MREG over the web using CSC services with a secondary aim to increase MREG usability via PulSeq development. Targeted researchers include neuroscientists, computer and MRI engineers.

Key words: computational medicine of ultrafast brain imaging, precision diagnostics & treatment, biosensors , glymphatics

• Head: Raija Korpelainen, Professor
• Factulty: Faculty of Medicine
• Group composition: 3 professors, 9 postdocs, 14 doctoral researchers
• Website: https://www.oulu.fi/en/research-groups/physical-activity-and-health-across-lifespan
• Contact: raija.korpelainen (at) odl.fi

The Physical Activity and Health Across the Lifespan is a multidisciplinary research group and our research combines life course epidemiology, sports medicine, urban design, health geography, geographic data science, and advanced modelling techniques to comprehensively explore the complex interplay between environmental exposures and health behaviors in non-communicable and infectious disease dynamics. Contemporary global health challenges originating from the increasing prevalence of physical inactivity, obesity, lack of nature contact and exposure to traffic emissions require reassessing especially population-based approaches to disease prevention. Given that changing population behavior patterns remain one of the greatest challenges for public health research, we focus on systems approaches addressing not only individual level factors, but also built and natural environments and policies.

A fundamental aspect of our research involves adopting a life course epidemiology perspective. This trajectory-based approach enables us to connect individuals' lives across time and space. By accurately quantifying health behaviors and exposure to different environmental factors throughout the life course, we aim to identify critical periods and cumulative effects influencing health outcomes. Regarding health behaviors, of interest are especially physical activity, physical inactivity, 24-hour activity cycle (including physical activity, sedentary behavior and sleep), diet, and tobacco and alcohol use. In terms of environmental exposures, the focus is on the diverse features of the urban form and natural environment such as neighborhood walkability, obesogenic environment, neighborhood socio-demographic factors, greenness, biodiversity, geodiversity and air pollution.

We will use prospective data from the Northern Finland Birth Cohorts 1966 and 1986 and Oulu45 cohort encompassing questionnaire, clinical examination, and register based information. These data have been augmented with geographic data derived from Finnish Community Structure Grid Database, Finnish National Road and Street Database, CORINE Land Cover, and USGS Landsat and assessed with Geographic Information System. In all these cohorts data on physical activity and sedentary behavior have been collected by self-report and accelerometer based devices. Besides traditional statistical techniques, our methods include longitudinal modelling, and utilization of structural causal models and Bayesian networks to foster a deeper understanding of the complex relationships between environmental factors, health behaviors, and disease outcomes.

Finally, our research emphasizes a transition from research findings to practical applications, providing urban planners with scientifically validated methods for monitoring and planning healthier environments. By integrating spatial data and geographic data science tools, we aim to empower practitioners and policymakers to make informed decisions that positively impact public health, which will ultimately contribute to the development of healthier and more sustainable future cities.

Key words: Environment, Health behavior, Health

• Head: Marja-Liisa Laitala, Professor
• Faculty: Faculty of Medicine
• Group composition: Oral hard tissue research group: 1 professor, 1 prof. emerita, 2 senior reseachers, 1 postdoc, 10 reseachers. DataAI research group: 1 professor, 2 senior researchers, 2 postdocs, 8 researchers. Measurement technique research group: 1 senior researcher, 4 researchers
• Websites: https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/research-unit-population-health
• https://www.oulu.fi/en/research-groups/industrial-measurements
• https://www.oulu.fi/en/research-groups/data-analysis-and-inference-group
• Contact: marja-liisa.laitala (at) oulu.fi

ML4OralHealth is a multidisciplinary research group focused on the prevention of oral hard tissue diseases in different age groups and populations utilising big data in oral healthcare. Our research group is comprised of oral healthcare professionals, oral epidemiologists, image technology experts and data scientists committed to investigating various solutions for improving oral health and well-being of populations. Our vision is to develop virtual eHealth services that offer individually tailored oral health services regardless of time and place thereby saving resources from healthcare.

The research group has been collecting and combining the oral health data including surveys, electronic health records, and intra-oral photographs. This research project will develop, validate and implement an artificial intelligence (AI) –-driven eHealth tool which predicts the risk of the most common dental diseases without expert involvement. Furthermore, this tool will be applied in offering personalized preventive oral health care recommendations and triaging them as per treatment urgency. To achieve this, an approach using powerful state-of-the-art machine learning, deep learning, computer vision, and data fusion methods utilising multisource data of intra-oral images and oral health surveys is proposed.  

The proposed topic provides challenging research tasks at the intersection between AI/ML techniques, data science, and analysis of large-scale multi-modal medical and healthcare data, and collaboration with multidisciplinary international team of oral health, data science, and measurement techniques experts.

Key words: risk assessment, oral diseases

• Head: Petri Lehenkari, professor
• Faculty: Faculty of Medicine
• Group composition: Professors: Petri Lehenkari (OU), Juha Tuukkanen (OU), Johannes Kettunen (OU), Petteri NIeminen (UEF), Reijo Käkelä (HY). Postdoctoral researchers: Mikko Finnilä (OU), Sanna Palosaari (OU), Elina Kylmäoja (OU), Antti Koskela (OU), Mustonen Anne-Mari (OU), Olli Helminen (OUH). Researchers: Jerry Ngyen, Anni Junttila, Rebekka Suni, Ville Palomäki, Janne Heikkinen, Inari Avila, Tomi Nousiainen, Asla Keisu, Markus Tuoma
• Website: https://www.oulu.fi/en/research-groups/bone-and-stem-cell-biology-research-group
• Contact: petri.lehenkari (at) oulu.fi

The Stem Cell Research Group comprises both clinicians and natural scientists, employing translational approaches to elucidate fundamental cellular processes underlying inflammation and healing, particularly in joint diseases. Our investigations involve the use of cultured patient-derived cells and tissues, complemented by clinical data from patients. Our primary focus has been on understanding the inflammatory processes that regulate the symptoms of osteoarthritis.

A distinctive aspect of our research involves a unique collection of patient data and materials from bariatric surgery patients. We believe that this collection holds the key to unraveling the mystery of how and why obesity contributes to joint diseases. The material includes blood samples, adipose tissue samples, joint imaging data, and clinical data related to joint symptoms.

Our prior work has demonstrated changes in the adipose tissue inflammasome in patients who successfully lose weight after surgery (doi: 10.1002/oby.23602). Our next objective is to identify the molecular mechanisms at the tissue level. Subsequently, we plan to use cohort data (NFBC1966) and a broader pool of genetic data (FinnGen) to validate whether the findings at the tissue level correlate with the population level. FINNGEN includes genome-wide data from 500 000 participants, who have complete follow-up through electronic health records spanning up to 50 decades. Records include drug prescriptions, diagnosis codes and procedure codes that enable a plethora of approaches to study the genetic effects. Using this unique data, we anticipate discovering novel genes and mechanisms linking joint symptoms to the regulation of inflammation and pain sensation.

Our primary hypothesis posits that in obese patients, adipose tissue undergoes inflammatory imbalance, leading to the production of systemic cytokines that trigger latent inflammation in joints, ultimately causing local symptoms in osteoarthritis. Supporting this hypothesis is the observed effect of bariatric surgery on joint symptoms prior to changes in weight alone. Emerging evidence suggests that adipose tissue macrophages could be the key players in this inflammatory imbalance. Therefore, we contend that our approach, combining tissue anatomy and histology with the analysis of regulatory genes at the population level, is the most effective means to unveil the underlying mechanisms in sufficient detail.

The next phase of our study will involve analyzing these recognized adipose tissue molecules and pathways in genetic data (FinnGen) and, where applicable, in cohort data. We are seeking a candidate with a deep understanding of inflammation-related pathways, capable of conducting gene analysis in large datasets, and skilled in further analyzing collected patient data. The position encompasses senior-level responsibilities in data collection, analysis, and supervision. We anticipate publishing several peer-reviewed articles in top-tier journals and expect the selected candidate to actively contribute to the writing process as an author.

• Heads: Aki Manninen, Professor & Gong-Hong Wei, Professor
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: 1 professor, 1 affiliated professor, 3 postdocs, 6 PhD Researchers
• Website: https://www.oulu.fi/en/research-groups/manninen-lab-cell-biology-and-genomics-prostate-cancer
• Contact: aki.manninen (at) oulu.fi

Our team focuses on characterization of transcriptional regulatory proteins, including transcription factors (TFs) and epigenome regulators and how their activities are orchestrated within the tumor microenvironment (TME). In this context we investigate the role of the integrin family of the extracellular matrix (ECM) receptors in the regulation of PCa genome organization. How the TME controls PCa genome to regulate PCa cell growth and invasive properties. Aberrant TF programmes and integrin signaling are often observed in PCa as well as in various other types of cancers. However, how these two events are linked remains incompletely understood. Understanding the misregulated transcriptional networks in cancer cells and clinical specimens or patient-derived organoids will facilitate the discovery of novel mechanisms and clinical biomarkers for cancer risk prediction and potential targets for therapy. Here we aim to uncover the cancerous role of multiple TFs in prostate cancer by integrated functional cell biology and genomics and clinical data analysis. Together with data from genome-wide association studies and validation using state-of-art cell biological models, we aim to explore how the cancer risk-associated single nucleotide polymorphisms alter the genetics of TF-DNA binding at sites of clinically important enhancers, thereby affecting gene expression programs and eventually PCa-TME interactions to regulate PCa susceptibility and progression. We will validate the key prioritized hits, including risk SNPs, enhancers, and causal genes relevant to integrin signaling pathways that are potential biomarkers and drug targets for precision cancer medicine. These projects involve handling and analysis of multiple different large datasets ranging from genome-wide NGS-data to protein-protein interaction-proteomics and drug-response datasets as well as bioinformatic data from publicly available databases.

Key words: Functional cancer genomics

• Head: Tuomo Mantere, PhD, Docent
• Faculty: Faculty of Medicine, Translational Medicine Research Unit
• Group composition: 1 Professor, 1 Senior researcher, 2 doctoral researchers
• Website: https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/research-unit-translational-medicine
• Contact: tuomo.mantere (at) oulu.fi

Leukemias represent a significant health challenge, comprising 2.5% of all new cancer cases and 3.1% of cancer-related deaths. They place considerable strain on healthcare systems, economies, and individual lives. Adult acute myeloid leukemia (AML) is particularly challenging, with a mere 15% of patients surviving beyond five years post-diagnosis. Leukemias, like other cancers, arise from genetic changes that disrupt normal cell functions such as growth, proliferation, and differentiation. A key mutational process in leukemia is genomic structural variation (SV), which includes chromosomal changes that can lead to deletions, amplifications, or rearrangements of genomic segments, ranging from kilobases to entire chromosomes. Detecting these SVs is pivotal also in the clinical assessment of leukemia patients, aiding in diagnosis, subtype classification, and informing prognosis and treatment strategies. Traditional methods, including chromosomal karyotyping, fluorescence in situ hybridization, chromosomal microarrays, and next-generation sequencing, are widely used but have significant limitations in fully assessing the complex SV landscape in diverse cancer samples.

Our cancer genetics research group has recently expanded its research projects to include optical genome mapping (OGM) for high-resolution SV detection. We applied this technique first in high-risk familial breast cancer cases and congenital genetic disorders, and now it is also being used for a variety of cancer samples, including solid tumors and hematological malignancies. Our preliminary studies indicate that OGM can identify both balanced and unbalanced SVs with remarkable resolution. Its cost-effectiveness also facilitates deep genome-wide molecular coverage, which is essential for detecting SVs in heterogeneous cancer samples. In this project, we aim to bridge the knowledge gap in AML's SV landscape by employing OGM for comprehensive, high-resolution cytogenomic profiling. This approach is complemented by additional big data analysis methods including whole-transcriptome sequencing (WTS) and whole-exome sequencing (WES). By integrating detailed cytogenomic SV profiles with WTS/WES data, our objective is to uncover genomic variation, novel mutated genes, disrupted pathways, oncogenic gene fusions, and to study the interplay between SV landscapes, genic mutations, and gene expression profiles. Utilizing samples from the National Finnish Hematological Biobank, we will correlate genomic and transcriptomic data with extensive clinical patient information, like treatment responses and survival rates. This could lead to the discovery of new, clinically relevant biomarkers and AML subtypes. Moreover, we aim to evaluate OGM's potential as a transformative cytogenomic diagnostic tool to enhance precision in leukemia patient care. Overall, using this novel combination of large-scale genomic analysis techniques, our research is poised to reveal new drug targets, improve patient outcomes, and refine diagnostic techniques in AML treatment and management.

Key words: Genomics, Transcriptomics, Structural Variants, Biomarkers, Leukemia

• Head: Dr Ali Mobasheri, Professor of Musculoskeletal Biology, University of Oulu
• Co-PIs: Dr Johannes Kettunen, Professor of Systems Medicine and Scientific Director of Biocenter Oulu, Dr Jonathan Larkin, Visiting Professor, University of Oulu, Dr Jaume Bacardit, Professor of Artificial Intelligence (Newcastle University)
• Faculty: Faculty of Medicine
• Group composition: 2 Professors, 1 Visting Professor, 1 Senior Postdoc/Research Manager, 3 Doctoral Researchers, 1 Collaborating External Professor. This collaboration also includes a consortium funded by the European Commission's Innovative Medicines Initiative (IMI), now known as Innovative Health Initiative. The consortium, which was originally launched by GSK, Merck and Servier incorporates a number of academic institutions, including the University of Oulu. We have deeply phenotyped a cohort of 300 patients with osteoarthritis, and we have access to an extensive dataset
• Websites: https://www.oulu.fi/en/researchers/ali-mobasheri
• https://www.oulu.fi/en/researchers/jonathan-larkin
• http://homepages.cs.ncl.ac.uk/jaume.bacardit/index.html
• Contact: ali.mobasheri (at) oulu.fi

Are you looking for a unique opportunity to apply your skills in computational data science at the cutting edge of personalised medicine and help shape the future of healthcare for a disease that affects nearly 600 million people worldwide?

Osteoarthritis (OA) is a heterogeneous disease representing one of the most prevalent and poorly addressed chronic medical conditions in the world. Characterized by chronic pain, structural joint degradation, disability, and impaired quality of life, OA drives an ever-growing burden on global health and economics. Current OA therapeutics are marginally effective and focus primarily on treating pain symptoms in a 'one-size-fits-all' treatment paradigm. This mindset not only ignores the heterogeneous nature of the OA patient population and does not address the underlying disease mechanisms within individual patients, but is recognized as a significant contributor to the failure of therapeutic clinical trials over the past few decades. Adding additional complexity, when used chronically these current treatments exhibit diminishing efficacy, and risk of life-threatening side effects, and increasing evidence suggests they contribute to the accelerated progression of structural joint deterioration. Therefore, there is an urgent need to identify and better characterize OA patient subsets to refine the current treatment paradigm and aid in the development of novel therapeutics using personalized medicine strategies.

The successful candidate will join a multidisciplinary team with expertise in Artificial intelligence, deep phenotyping, disease biology, biochemical markers, imaging biomarkers, muli-omics, and clinical therapeutic development to advance the understanding of OA patient subgroups and how they should be most effectively and safely treated. This international team of collaborators has worked together for nearly a decade to set the foundation for the effort and generated/collected numerous large and robust multi-omic, imaging, and clinical datasets from observational and therapeutic OA trials. The group now seeks to harness the insights contained within these datasets and apply them to improve the lives of patients by designing novel AI strategies to match treatment to patients. To achieve this aim we have to face several challenges that will provide invaluable experience to the candidate: multi-modal 'big data' harmonization, insights into data privacy and ethics policies, advanced and novel computational methods (Machine Learning and Explainable Artificial intelligence) in order to generate and validate hypotheses for understanding OA pathophysiology and patient stratification. Candidate(s) will also be influential members of teams working to design and conduct clinical studies to assess the efficacy and safety of new and existing therapeutics to provide an applied 'bench-to-bedside' experience that will enhance the candidates' subsequent career prospects in the academic, clinical and/or industrial drug development sectors.

Although OA is the primary disease focus of this effort, the techniques employed and competencies gained are widely applicable to any disease indication and will expand the applicants' experience for future opportunities and provide direct exposure to a wide range of collaborators from academic, clinical, and industrial fields. Opportunities also exist within the project timeframe for international secondments to increase interaction with collaborators, gain exposure to different sectors, and further broaden the candidate's overall skillset and network.

Key words: Artificial Intelligence, Data integration and harmonization, Clinical trial design and execution

• Head: Jukka Moilanen, Professor; Elisa Rahikkala, associate professor
• Faculty: Faculty of Medicine
• Group composition: 1 professor, 1 associate professor, 1 senior researcher (principal investigators in subprojects), 2 postdoctoral researchers, 4 PhD students, and other researchers specific to subprojects from the University of Oulu and associated public partners Oulu University Hospital and the Genetics Laboratory of Nordlab Oulu
• Website: https://www.oulu.fi/en/research-groups/genetic-diseases-northern-finland-finndig
• Contact: jukka.moilanen (at) oulu.fi

Finland provides excellent opportunities for novel genetic discoveries due to unique population history. In particular, the population of Northern Finland has evolved from a small set of founders and is genetically homogenous and enriched with many rare disorders. While individually rare, rare diseases as a group represent a significant burden to healthcare. Recent advances in genomics have led the research of these diseases to the realm of Big Data, with massive amounts of data being produced from patients and their family members, interpreted with bioinformatic and other computational methods, and collected into vast databases. Management and interpretation of genomic data in a multidisciplinary setting with clinical geneticists, clinical laboratory geneticists and translational researchers, with an understanding of the relevant clinical, pathogenetic and ethical issues, are essential skills for future professionals and researchers in genomic medicine.

Our research aims to elucidate the genetic causes, pathogenic mechanisms, and clinical characteristics of rare diseases in Northern Finland. The PIs and senior researchers are clinical geneticists with significant experience in clinical and molecular genetic diagnostics, genetic counselling, variant interpretation, bioinformatic tools, ethics, healthcare policy making, and research. Clinical and genomic data are collected among patients of the catchment area of Oulu University Hospital (OYS) covering approximately 45% of Finland's geographical area. All services in clinical genetics and genomics in the area are provided by the Department of Clinical Genetics of OYS and the Genetics Laboratory of Nordlab Oulu, which are public partners of the research. As such we have access to a highly comprehensive research cohort with clinical and genomic data from this unique population. Furthermore, the widely known FinnGen project provides population-level data on common and rare variants and disease associations among Finnish biobank donors, and these data can be utilized also in the research of rare diseases. We also participate in European Reference Networks (ERNs), especially ITHACA and GENTURIS, enabling collaboration with other European researchers. GeneMatcher is used to expand our international collaboration and facilitate novel gene discoveries.

Our research programme includes several disease group specific cohort studies. Currently, the largest cohort focuses on intellectual disability (ID). Despite modern diagnostic techniques, the cause of ID remains unknown in a significant number of patients. Northern Finland Intellectual Disability (NFID) is a large collaborative research project with Helsinki University. NFID includes detailed clinical and whole exome sequence (WES) data from more than 1000 families with ID of unknown aetiology. We integrate data from publicly available databases, including gnomAD, Decipher and SysNDD, with WES data of the NFID cohort, to identify novel pathogenic variants. Clinical data, including the Human Phenotype Ontology terms, are used to prioritize clinically relevant variants. If applicable, further functional analyses, such as RNA sequencing and transcriptome analysis, proteomics analysis or methylation signatures, are used to clarify the significance of the variants. Validated causal findings are returned to the patients and their families in genetic counselling. Our research demonstrates that Big Data and multidisciplinary collaboration can facilitate patient-centered, truly personalised healthcare with tailored management strategies, and, hopefully, the development of targeted therapies.

Key words: rare diseases, genomics, bioinformatics

• Head: Johanna Myllyharju, Professor
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: 1 full professor, 2 adjunct professors, 1 senior post doc, 4 PhD researchers, 2 technicians
• Websites: https://www.oulu.fi/en/researchers/johanna-myllyharju
• https://www.oulu.fi/en/university/faculties-and-units/faculty-biochemistry-and-molecular-medicine/ecm-and-hypoxia
• Contact: johanna.myllyharju (at) oulu.fi

Collagens are the most abundant proteins in our body making up to 30 % of the total protein mass. They are the major and critical constituents of all connective tissues. The collagen family includes 28 distinct collagen types and their triple-helical structure is formed by collagen polypeptide chains that are encoded by altogether 46 genes. Collagen synthesis involves several unique and vital post-translational modifications (PTMs) catalyzed by highly specialized enzymes that typically have several isoenzymes. Abnormalities in the primary structure, amount, supramolecular assembly, and PTMs of collagens have important roles in many diseases and pathological conditions, ranging from rare hereditary connective tissue diseases to abnormal wound healing, fibrosis and cancer. We are a world-known expert group on collagen synthesis and their PTM catalyzing enzymes. We utilize a wide repertoire of methodologies in our research, encompassing molecular biology, biochemistry, enzymology, cell biology, recombinant expression technologies, genetic engineering of cells, gene-modified mice and a large number of pre-clinical disease models.

In this project our aim is to understand cell and tissue type specificities in the generation of the critical collagen PTMs under healthy and selected disease conditions, particularly fibrosis and cancer. To achieve this we will have two main objectives. Firstly, our aim is to understand regulatory connections between the expression of various collagen types and their modifying enzymes. We will perform transcriptomic analyses of RNA-seq data from various mouse and human cell and tissue types under normal and selected disease conditions. We will generate both original in-house cellular and tissue data taking into account also potential temporal expression differences and challenging with hypoxic conditions, as well as existing transcriptomic data available in open access data repositories. Secondly, our aim is to decipher the PTM signatures of various collagen types, connect them to individual isoenzymes of the collagen enzyme machinery, and analyse changes in the PTM signature that occur during normal development and under selected disease conditions. For this we will generate proteomic data of the collagen PTMs by mass spectrometry from the same cell and tissue material generated for the transcriptomic analyses. In addition, we will utilize existing large-scale proteomic data sets available in open access repositories. This project is inspired by our recent initial work showing that isoenzymes catalyzing the critical modification of prolines to 4-hydroxyprolines in the repeating collagen -X-Pro-Gly- triplets show distinct isoenzyme specificity (Salo AM et al., under final revision, https://www.biorxiv.org/content/10.1101/2023.06.28.546674v1).

Abnormalities in collagen synthesis are involved not only in rare hereditary connective tissue diseases, but also in several common diseases, most importantly fibrosis and cancer. Big data -based, deep cell and tissue level understanding of the details of the operation of the collagen synthesis machinery would have important implications for the future development of novel therapeutics, either to correct deficiencies in collagen synthesis and assembly or to block collagen synthesis in a targeted manner in various fibrotic diseases and cancers.

The project will be supervised by Professor Johanna Myllyharju and Adjunct Professor Antti M. Salo.

Key words: extracellular matrix, fibrosis & cancer, omics

• Head: Teemu Myllylä, Associate Professor (Tenure track), Doctor of Science
• Faculty: Faculty of Medicine
• Group composition: 2 professors, 3 PostDocs, 6 PhD students, 1 assistant personal (MSc), 3 MSc students
• Website: https://www.oulu.fi/en/research-groups/myllyla-group
• Contact: teemu.myllyla (at) oulu.fi

Our research group focuses on developing and exploiting state-of-the-art multimodal technology to provide new tools and methods for medical use. Currently, developed techniques enable recording simultaneously brain’s hydro-, hemo- and electrophysiological dynamics, while continuously monitoring cardiovascular, blood pressure and respiratory signals. The main technologies we work in are functional near-infrared spectroscopy (fNIRS), diffuse correlation spectroscopy (DCS), ultrasound, photo-acoustic and microwave imaging and a variety of multimodal wearable sensors. These we can combine with existing medical imaging technologies. Our novel technology can be utilized in medical and translational research and therapies, on a wide range of topics.

At present, Prof. Teemu Myllylä and his team members are pushing the boundaries to measure brain activity using wearable novel technology. In close collaboration with Oulu University Hospital, one of our main research topics is to develop methods to measure early signs of neurodegenerative diseases (NDD) before any symptoms appear. Our device, Glymphometer, utilises light to determine neurofluid dynamics and possible disorders it causes in the brain, providing real-time assessment of a patient’s brain health. For instance, at present, the device is capable of distinguishing Alzheimer’s Disease (AD) patients from healthy controls based on a 5 minutes measurement. The next phase of our research involves determining how early we can detect the risk of AD or other NDDs before any symptoms appear, which would be a significant breakthrough in the field.

Multimodal data analysis using machine learning approach are developed to determine so called Glymphatic index values, which reflect normal aging, increased of risk of NDD, and different stages of AD. This requires mass-scale clinically approved personalized bio/data bank collection including cohort. At present and next three years we are collecting with the Glymphometer human brain data from patients with AD and MCI. The data collection is conducted with several clinical collaborators in Europe. In addition, it is crucial for the machine learning approach that we collect data also from healthy subjects (controls) with same age. For this, 66 cohort data is of high intrerest to include for the data collection. Moreover, particularly in this age group risk of NDD and AD typically start to increase. Another objective is to study how sleep, stress management, and recovering from stress are linked with brain health and glymphatic index values. The key in the process is also to enable clinically valid multimodal data collection continuously in “real-world” situations and in all locations. This will be a major opening for the new P4 (Predictive, Preventive, Personalized and Participatory) medicine era. Our research also contribute to the “Theme 1) Data as new resources in the society”, since we focus on the practical solutions which health and wellbeing data may offer, as well as utilize data originated from wearable sensors. In general, our aim is to understand health and disease and to study disease risks and prediction models by combining multiomic health and wellbeing data sets generated from various sources.

• Head: Terhi Piltonen, Professor
• Faculty: Faculty of Medicine
• Group composition: 1 professor, 4 post docs, 14 researchers and 1 statistician
• Website: https://www.oulu.fi/en/research-groups/focus-female-health-long-term-health-outcomes-related-common-gynecological-conditions-pcos
• Contact: terhi.piltonen (at) oulu.fi

Welcome to one of the PCOS-hotspots in the world! We are looking for an expert on epidemiological or bioinformatics related data analysis . Professor Terhi Piltonen and her team has focused on studying the lifelong health of women with PCOS for over 20 years. PCOS (polycystic ovarian syndrome) is the most common metabolic and reproductive disorder in women affecting one out of eight women. There is high 50-70% heritability of the syndrome, yet the data on early origins and the outcomes in later life are scarce. Overall, the syndrome translates into vast comorbidity risk including health impairment affecting metabolic, reproductive, and mental functions. Some of the adverse health outcomes seem to accumulate also to the first-degree relatives. Our team aims on assessing early life factors, comorbidity risks and lifelong health in women with PCOS and their relatives.

The project has several globally unique data sets available to answer the study questions. We have an excellent international network that also facilitates data merge and meta-analysis. 1) The Finnish national register database. The overall health impact and comorbidity will be assessed in register data set, which includes women with PCOS in Finland (~25 000) and 1:3 ratio matched controls as well as their first-degree relatives. The national registers include data on any health conditions and deaths, data related to reproductive aspects i.e. infertility, pregnancy complications and deliveries, data on social support and work ability. 2) The Northern Finland Birth Cohort. The longitudinal health will be assessed in two birth cohorts that have PCOS cases identified; the Northern Finland Birth Cohort 1966 (NFBC66) and 1986 (NFBC86) with over 20 000 participants and data that reaches from birth till adulthood https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/…). 3) WENDY. More detailed health outcomes will be studied in WENDY (Women´s health study) dataset that our team recently finished on collecting. WENDY-data is one of the largest datasets focusing on gynecological and metabolic health in women including 2000 women from NFBC1986 with >10 000 collected variables, birth cohort data linkage from neonatal period up till adulthood and linkage to national registers (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/…).

I addition to this we have also created various PCOS mouse models and harvested different tissues for genetic analysis to investigate comorbidities related to the syndrome. To assess the mechanism and factors behind reproductive comorbidity, including subfertility and pregnancy complications, in affected women, we have established endometrial and placental tissue banks. Our tissue bank beholds human (PCOS and control) endometrial and placental tissue samples as well as tissue-derived organoids from which we have collected RNA-sequencing data.

We are looking for team members experienced in working on large epidemiological datasets, preferably also on longitudinal settings or with skills with single-cell/bulk RNA-sequencing data. In return we offer highly enthusiastic translational team with excellent national and international networks to improve health outcomes for women with PCOS. 

Key words: PCOS, Epidemiology, Women´s health

• Head: Ville Pimenoff, PhD, Adjunct Professor in Evolutionary Medicine
• Faculty: Faculty of Medicine
• Group composition: 1 Professor, 1 Adjunct Professor, 2 Senior Research Fellows, 6 Postdocs, 10 PhD students, 5 Research assistant
• Websites: https://www.oulu.fi/en/researchers/ville-pimenoff
• https://www.oulu.fi/en/researchers/sylvain-sebert
• https://www.humanexposome.eu/
• Contact: ville.pimenoff (at) oulu.fi

Estimating virome and microbiome exposures that are intrinsic to our own environment, be it the air we breathe, the food we eat or the interactions we foster. However, little is known about the longitudinal composition or dynamics of microbial exposures in our lives, nor how they have influenced our susceptibility to disease. This is in part because we still mostly lack individual longitudinal data of the short and long-term virome and microbime environmental exposures. Systematic longitudinal sampling will fill this gap by enabling the analysis of personalized virome and microbiome estimation using longitudinal deep sequencing of human samples..

Understanding differences between the short-term (in days to months) and long-term (in years and decades) timescales of the environmental virome and microbiome exposure interplay is particularly important to identify the drivers of our interaction with the environment. Ultimately, our findings may help to promote new diagnostic and therapeutic strategies against environmental exposure-related pathogens and associated diseases.

The Life-course Epidemiology is an interdisciplinary research group in the niche of combining longitudinal population cohort and deep sequence data jointly with biobank registry-based archives of birth cohort and environmental data for comprehensive longitudinal dissemination of environmental virome and microbiome exposures in human health and disease (LongiTools, TRIGGER, HEAP). The core group consist of life-course epidemiologist, biostatisticians, exposure assessment, population genetics and epigentics, microbiology and bioinformatics. For the proposed project we are also collaborating with groups whose research is focusing in modelling regulatory mechanisms in cancer (Ass. Prof. Valerio Izzi), applying deep learning AI algorithms for molecular data (Prof. Juha Röning) and detecting extracellular vesicle -related cellular signaling (Prof. Seppo Vainio).

We are using longitudinal human metagenome data from different body sites from different population cohorts, biobank-based birth cohorts and large-scale cancer atlas sample collections to make significant contributions to the following research objectives:

1. Personalized virome and microbiome exposure estimation using longitudinal metagenome data from various population cohort.

Personalized longitudinal virome and microbiome profiling will eventually change healthcare by enabling health monitoring at the different precision. Our objective is to demonstrate that using longitudinal virome and microbiome targeted metagenomes will enable high-resolution personalized biotic exposure estimation. In this project we will use the state-of-the-art deep sequenced metagenome data with advanced bioinformatic and machine learning tools to build strain-level virome and microbiome exposure pattern estimations in parallel with the population cohort and national health registry -based epidemiological data.

1. Multi-Omics and AI inference of microbial associations in health and disease

The combined long and short read high-throughput sequencing and bioinformatic tools to analyze large number of samples in high DNA-level precision of virome and microbioem exposure have drastically enhanced our analysis of human exposures in health and diseases. Furthermore, combining these metagenomic methods with detailed serial sampling have enabled to assess the role of biotic associations in different diseases and to model microbial interactions at organ level. For this project we will leverage advanced metagenome pipelines for fine resolution multi-omics and pangenome analysis. Advancer AI inferences we will execute in collaboration with Juha Röning's team. 

Key words: bioinformatics, Pan-Domain, metagenomics, phylogenetics, exposome

• Head: Eija Pirinen, Associate Professor (tenure track)
• Faculty: Faculty of Medicine
• Group composition: Head: Associate Professor Eija Pirinen, 3 Professors, one docent, 2 postdocs, 5 PhD students, 3 technicians
• Website: Mitochondria and nutrient signalling lab (MitoNuts) https://www.oulu.fi/en/research-groups/mitochondria-and-nutrient-signaling
• Contact: eija.pirinen (at) oulu.fi

Mitochondria and nutrient signalling lab (MitoNuts) is enthusiastic and interdisciplinary research group dedicated to study molecular mechanisms behind mitochondrial dysfunction in obesity and its related metabolic diseases, and cancer cachexia, and to develop new mitochondrial activating treatments. MitoNuts brings together experts with diverse background and skills; basic scientists, exercise physiologists, pharmacists and clinicians, to find solutions to improve diagnosis, prediction and treatment of obesity and its related metabolic diseases, and cancer cachexia.

MitoNuts’s special expertise is the modelling of obesity in vitro using unique three-dimensionally cultured human adipocyte spheroids. The integration of data from multiomics analyses (single-cell RNA sequencing, transcriptomics, metabolomics and proteomics) from this study model allows the team to capture the understanding of molecular underpinnings of obesity-related mitochondrial dysfunction and metabolic complications. This type of novel information can help to move beyond the current trial-and-error clinical assessment of pharmacological treatments to more targeted treatments (data-driven medicine). Thus, the obtained results can pave the way to the development of targeted mitochondria-based therapies for obesity and its related metabolic diseases.

Accurate risk prediction models for cardiovascular diseases are important for facilitating early diagnosis and prevention of cardiovascular events. The goal of MitoNuts is to develop a mitochondria-based risk prediction tool using large-scale clinical and molecular data (blood cell transcriptomics data and circulating mitochondrial DNA amount) from a follow-up study in patients with hypertension and healthy controls to identify novel molecular risk factors for cardiovascular events. This type of predictive models can help to identify at-risk populations and to formulating early and personalized intervention strategies.

Lastly, MitoNuts aims to identify novel mitochondria related biomarkers for cachexia which is a severe wasting syndrome worsening the survival and the outcome of the cancer patients. The diagnosis of cachexia before the manifestation of muscle wasting, is one of the current major challenges in the clinic. The team will perform computational analyses to recognize complex mitochondria-related molecular patterns in different cellular information layers (muscle tissue transcriptomics, metabolomics and proteomics datasets) in patients with cancer cachexia versus their healthy controls. These studies can potentially introduce novel cachexia biomarkers and consequently, facilitate the early identification of cancer patients with cachexia, speed up the treatment of their symptoms and improve their prognosis.

The scientific impact of the projects described here can be substantial. They have a great potential to become a break-through in the research and drug discovery of mitochondrial dysfunction, and eventually in the management of obesity and its related metabolic diseases, and cancer cachexia. Finally, the described projects are excellent examples of cross-disciplinary research where cutting-edge basic science is combined with translational, clinical and computational research and thus has a great potential for scientific breakthroughs.

Key words: mitochondrial dysfunction, metabolic diseases, treatment

• Head: Renata Prunskaite-Hyyryläinen, Assistant Professor
• Faculty of Biochemistry and Molecular Medicine
• Assistant Prof. Renata Prunskaite-Hyyryläinen group: 1 professor, 1 postdoctoral researcher and 3 doctoral researchers
• Website: https://www.oulu.fi/en/researchers/renata-prunskaite-hyyrylainen,
• Lab's website: https://www.oulu.fi/en/research-groups/renata-prunskaite-hyyrylainen-reproductive-and-molecular-biology
• Contact: renata.prunskaite (at) oulu.fi

The candidate will participate in cutting-edge research in reproductive biology, identifying genes, and molecular mechanisms, associated with both male and female fertility as well as related comorbidities by exploring human open access and in-house databases and disease-associated mouse models. The project will enable work in two research groups: assistant prof. Renata Prunskaite-Hyyryläinen at the Faculty of Biochemistry and Molecular Medicine and prof. Terhi Piltonen at the Faculty of Medicine.

Idiopathic fertility disorders are associated with mutations in various genes, however, multiple specific genes and mutations remain undiscovered. Assistant prof. Renata Prunskaite-Hyyryläinen’s team screens for deleterious gene variants associated with male infertility using in silico databases and constructs in-house infertile men whole exome sequencing (WES) cohort. The discovered genes are further studied in gene-specific transgenic mouse models for in-depth functional research. The research work in Prunskaite-Hyyryläinen lab already revealed several novel testes-specific genes critical for male fertility that were previously poorly or not characterized at all, such as Cfap97d1 (PMID: 32785227) and Mrnip (PMID: 35920200). We also contribute to international efforts to report testes-specific genes whose deletion has no essential effect on fertility (PMID: 27357688).

On female fertility, the team of assistant prof. Prunskaite-Hyyryläinen is specifically interested in uterine morphogenesis, receptivity, and the changes in uterine morphology during early pregnancy. The lab develops 3D visualization methods to enable dynamic studies of uterine development such as genetically predisposed 3D uterine gland morphology (PMID: 34099644) and 3D extracellular matrix dynamics during early implantation. In this project, we use in silico databases to identify genes critical for female fertility and study them in vitro and in vivo models. We aim to understand the extracellular matrix's role in fertility establishment (3D MOUSEneST PMID: 39023143), maintenance, and pathology. We have a track record of uterine-specific gene studies in mouse models and cell cultures (PMID: 26721931, PMID: 34099644, and PMID: 28324064). The candidate will play a central role in gene and genetic variant discovery using the in silico and in-house WES, bulk, and single-cell RNA-sequencing databases of human and related model organisms created for functional studies of selected variants. Our studies on male and female fertility also extend to quantitative proteomics analysis.

Furthermore, the project will enable work with the research group of prof. Terhi Piltonen at the Faculty of Medicine, (https://www.oulu.fi/en/research-groups/focus-female-health-long-term-health-outcomes-related-common-gynecological-conditions-pcos). Professor T. Piltonen and her team, consisting of 4 postdoctoral researchers and 15 doctoral researchers, have focused on studying the lifelong health of women with PCOS (polycystic ovarian syndrome) for over 20 years. PCOS is the most common metabolic and reproductive disorder in women, affecting one out of eight women. The syndrome presents with vast comorbidity risk including health impairment affecting metabolic, reproductive, and mental functions. To date, the mechanisms behind the impaired reproductive functions and PCOS-related comorbidities are only scarcely characterized. Given that the clinical PCOS phenotype varies, it would be important to identify individuals with higher morbidity risk. In this project, Professor Terhi Piltonen utilizes the FinnGen database (https://www.finngen.fi/en) linked with clinical data to identify genetic variation between the identified PCOS clusters/phenotypes that could also translate into different comorbidity profiles. Recently, a group of prof. Terhi Piltonen created several PCOS mouse models and harvested different tissues for genetic analysis to investigate comorbidities related to the syndrome. To assess the mechanism and factors behind reproductive comorbidity, including subfertility and pregnancy complications, in affected women, the team established uterine endometrial and placental tissue banks. The created tissue bank beholds human (PCOS and control) endometrial and placental tissue samples as well as tissue-derived organoids from which prof. T. Piltonen’s lab collected RNA-sequencing data for further studies in this project.

The candidate will be able to contribute to studies of both research labs aimed at discovering genetic and molecular bases of infertility and related comorbidities. We are looking for a team member with a degree in bioinformatics, computational biology, molecular biology, or similar, with strong skills, especially in single-cell/bulk RNA-sequencing data and WES analyses. Experience with proteomic data analysis will be viewed as an advantage. Interest in developing and further expanding know-how will be encouraged and supported during the projects. In return, we provide a vibrant research environment spanning two research groups in two different faculties on the same campus. The candidate would become a part of highly enthusiastic research teams with excellent national and international collaboration networks.

Key words: primary infertility; genes/variants implicated in fertility control; WES, RNA-seq, and proteomics data analysis

• Head: Katri Pylkäs, Professor
• Faculty: Faculty of Medicine, Translational Medicine Research Unit
• Group composition: 2 professors, 3 postdocs, 4 doctoral researchers
• Research group website: https://www.oulu.fi/en/research-groups/breast-cancer-genetics-research-group
• Contact: katri.pylkas (at) oulu.fi

Breast cancer is the most common malignancy in women: yearly 1.7 million women are diagnosed with it and 500 000 breast cancer related deaths are reported worldwide. Breast cancer is strongly influenced by hereditary risk factors, but the known predisposing alleles explain less than half of the hereditary component, leaving causal factor for the majority of breast cancer families unknown. Our research focuses on resolving the molecular genetic factors and biology behind unexplained inherited breast cancer susceptibility; their significance in getting the disease and its treatment. For this, we use breast cancer cohorts from the genetically isolated Northern Finnish population.

In this project, multiple approaches for unravelling molecular genetics and biology behind breast cancer predisposition are used. For the discovery of rare germline breast cancer predisposing variants, we use whole-exome sequencing (WES) and optical genome mapping (OGM). WES is created with Twist Bioscience Comprehensive exome kit using NextSeq550 platform (Illumina), and SOPHiA DDM pipeline is used to call point mutations, small indels and read count based CNVs (≥ 2 exons). The identification of hitherto hidden genomic structural variants (SVs) is done using OGM (Saphyr, Bionano Genomics). OGM can detect large SVs (>50 bp), including those that are challenging to identify by sequencing or cytogenetics. The clinical significance of the identified variants is evaluated using additional, geographically matched cohorts (familial breast cancer cases, cases unselected for family history of cancer and age at disease onset, and disease-free controls) and associated clinical data. The functional effects of the identified, and the already established moderate-to-high risk variants, are investigated with functional genomics, biochemical and tumor modelling approaches, combined with the investigation of the mutational landscape of the associated tumors using both sequencing and OGM. Resolving genetic defects behind inherited predisposition refines clinical risk assessment for high-risk individuals. Further, understanding mutation’s functions at cellular level and their effect on the tumor mutational landscape can promote the development of more targeted therapies.

Key words: genomics, inherited cancer predisposition, breast cancer, exome sequencing, optical genome mapping, functional modeling

• Head: Lloyd Ruddock, Professor
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: 1 Professor, 1 docent, 3 postdocs, 7 PhD students, 1 technician
• Website: https://www.oulu.fi/en/university/faculties-and-units/faculty-biochemistry-and-molecular-medicine/protein-and-structural-biology
• Contact: lloyd.ruddock (at) oulu.fi

Disulfide bonds (DSBs) are essential, structure stabilizing, bonds found in secreted and outer membrane proteins. The majority of protein based therapeutics contain DSBs and the complexity and cost of their production is a major limiting factor in making them accessible and affordable. The group has been working for >20 years on the mechanisms of disulfide formation along with more recently the application of this knowledge using synthetic biology approaches to generate protein cell factories for the efficient production of DSB containing proteins in the cytoplasm of E.coli. Our CyDisCoTM technology has been patented and licensed for use in pharmaceutical production. We have reported multi-gram per litre yields of human proteins with DSBs as well as the production of proteins with up to 44 DSBs, in the cytoplasm of E.coli using CyDisCoTM.

We hypothesized that understanding the molecular mechanisms for DSB formation will allow the generation of more efficient protein cell factories for the production of biological drugs and that there is an untapped wealth of information in the disulfide bond-ome (DSBome). In addition, we hypothesized that the narrow view of the field to a small handful of organisms may have biased our understanding of the possible mechanisms of disulfide formation.

Using Alphafold structures we have reconstituted the DSBome of >300 organisms and identified a wealth of new data on the number and types of DSBs (>1.5 million DSBs), pathways for their formation and potential novel regulatory mechanisms. The data has been cross validated against all PDB structures and by bioinformatic conservation. Validation has also been undertaken in vivo, including the generation of a novel protein cell factory able to make pharma targets in higher yields than published CyDisCoTM variants.

This project will expand on one aspect of this work, using the DSBome information to identify substrate specificities of the machinery for DSB formation (e.g. mammals have >20 PDI family members). We have already identified that the N- and C-terminal cysteines of both consecutive and non-consecutive disulfide bonds show different motif patterns around the cysteine i.e. the four cysteine types in DSBs are non-equivalent and shown that the DSBome is a unique fingerprint for an organism. Using organism specific factors, combined with the pathways for DSB formation in each species, we aim to determine the substrate specificity of each folding factor, with experimental validation performed in parallel within the group. We believe this information will lead to both improved protein cell factories for protein based therapeutics and novel intervention strategies against disease causing organisms.

The project will require advanced skills in Python (incl. packages such as "pandas", "Biopython" and "scipy") and would benefit from knowledge in Bash or other command line language skills. A strong understanding of protein structure/function is important as well as familiarity with protein databases and statistical thinking.

The work in the group is currently funded by EU, NIH, Research Council of Finland, Business Finland, an industrial collaborative project and as a Biocenter Oulu Spearhead project (2024-2028).

Key words: protein folding, alphafold, disease intervention

• Head: Anatoliy Samoylenko, Senior Research Fellow
• Faculty: Faculty of Biochemistry and Molecular Medicine
• Group composition: 3 Profs. and 1 Senior PhD.
• Website: https://www.oulu.fi/en/research-groups/developmental-biology-laboratory-organogenesis-extracellular-vesicles
• Contact: anatoliy.samoylenko (at) oulu.fi

Bioelectromagnetic research targets how the electromagnetic fields, particularly microwaves, interact with living cells. It delves into the dynamic interplay between microwave radiation and living organisms focusing on the potentials of these fields to affect cells through both thermal and non-thermal mechanisms. The EU H2020 FET OPEN project (GLADIATOR) program dedicated to designing and developing advanced radiofrequency (RF) applicators, incorporating custom-engineered antennas and RF generators, a sophisticated software-defined radio (SDR) platform and control program. These applicators were designed to seamlessly meld with various cellular environments, ranging from standard Petri dishes to more complex systems like the Quasi Vivo® platform, to conduct also organoid and organ-on-chip studies. The results indicated controlled extracellular vesicles (EV) release that can be used for novel therapeutic application developments. We also will design and test novel magnetic nanoparticles (NPs) exploring thermal effects of RF exposure. This EU Data4Healthcare postdoc project aims to continue research to control and analyze the cells' behavior for the targeted therapeutic of tumors such as glioblastoma and renal cell carcinoma.

The methodological core of this research hinges on the meticulous control of RF system parameters - encompassing frequency, power level, modulation type, signal bandwidth, and duration - managed via comprehensive radio control software in conjunction with reconfigurable antennas. The incorporation of a dynamic feedback loop in our experimental setup is critical for real-time adjustments, ensuring the precision of our RF exposure conditions. Moreover, this system integrates a multifaceted approach to data collection, encompassing image analysis, clinical data analysis, and scrutinizing biological behaviors, to build an understanding of the microwaves exposure impacts. Developing such a system in a loop incorporating data collection, pattern analysis, machine learning algorithms, and biological data analysis results in an innovative therapeutic approach.

Our project is anticipated to make significant contributions to the field of bioelectromagnetic research, potentially revolutionizing our understanding of how cells respond to electromagnetic fields. The implications of this research are vast, extending into the realms of precision medicine, where it could herald new therapeutic modalities, including improved cancer thermotherapy, thereby reshaping our approach to healthcare and disease treatment in the context of electromagnetic field exposure. Putative secondments: NTNU (Norwegian University of Science and Technology), Bittium Oy, Nokia Oy.

Key words: genomics, radiofrequency, extracellular vesicles

• Head: Sylvain Sebert, Professor
• Faculty: Faculty of Medicine, Research Unit of Population Health
• Group composition: 1 professor, 2 senior research fellows, 1 Project Researcher, 2 Biostatisticians, 6 postdoctoral researchers, 10 doctoral researchers, 1 coordinator
• Website: https://www.oulu.fi/en/research-groups/life-course-epidemiology
• Contact: sylvain.sebert (at) oulu.fi

As the global population ages, we are witnessing the emergence of new patterns of disease clusters, known as multi-morbidity, which pose significant challenges to healthcare systems and impose a considerable burden on individuals. The World Health Organization defines multi-morbidity as the co-existence of two or more chronic conditions in the same individual. While this definition is useful for highlighting the scale of the issue, it lacks operational clarity in healthcare settings, as it does not specify distinct biological or pathophysiological processes.

Research indicates that the occurrence of multi-morbidity is not random; however, substantial knowledge gaps remain regarding the genetic and environmental determinants that contribute to these patterns within the population. Understanding these determinants is crucial for developing effective interventions and healthcare strategies.

In this context, the Life-Course Epidemiology research group at the University of Oulu is at the forefront of investigating the trajectories of health and ageing. The group leads several significant research initiatives, including the LongIToolshttps://longitools.org/) and STAGE (https://stage-healthyageing.eu/) projects, as well as the Northern Finland Birth Cohort Research Programme. These projects aim to understand, define, and ultimately prevent the life-course patterns of multi-morbidity. The research team combines expertise in various fields, including epidemiology (genetics, social, and environmental factors), statistics, life-course modelling, biology, and public health, to adopt a multidisciplinary approach to this complex challenge.

Through the Data4Healthcare programme, we invite applications to join our team in studying the genome-wide structure of multi-morbidity. Our research will employ both statistical and genome-wide methods to achieve two primary objectives: (i) to define longitudinal patterns of multi-morbidity using nationwide electronic health records, and (ii) to determine whether these observed patterns can be explained by genome-wide data from large datasets such as FinnGen, the UK Biobank (UKBB), and the Estonian Biobank.

To enhance our understanding of the causal relationships linking multi-morbidity, we will also utilise causal modelling techniques, including life-course Mendelian randomisation and genetic structural modelling. These approaches will help identify potential shared genetic determinants and clarify the causal pathways involved in multi-morbidity.

This research presents significant scientific challenges and is expected to yield unique discoveries in the field of complex diseases. It will be conducted within a well-established team and a robust European network, providing excellent scientific and career development opportunities. Our team possesses specialised knowledge in analysing national registers and large genetic datasets, ensuring effective support and knowledge transfer throughout the research process.

By addressing the intricate interplay of genetic and environmental factors in multi-morbidity, our work aims to contribute to the development of targeted interventions that can improve health outcomes for ageing populations. Ultimately, this research seeks to enhance our understanding of multi-morbidity and inform healthcare policies that can better accommodate the needs of individuals facing multiple chronic conditions.

Key words: Genetics, Genomics, Life-course mendelian randomization, Electronic Health Records, Multi-morbidity

• Head: Mikko J. Sillanpää, Professor; Patrik Waldmann, Associate Professor
• Faculty: Faculty of Science
• Group composition: 2professors, 5 post-docs, and 9 PhD students
• Websites: https://www.oulu.fi/en/university/faculties-and-units/faculty-science/mathematical-sciences
• https://www.oulu.fi/en/research/dynamic-data-modelling
• https://cc.oulu.fi/¨misillan
• https://www.oulu.fi/en/researchers/patrik-waldmann
• Contact: mikko.sillanpaa (at) oulu.fi

In the computational statistics and AI group, our general objective is to develop computationally efficient methods for high-dimensional big data problems which cannot be solved without applying prior information or regularizing the problem with penalty terms. Of particular interest are methods and tools for longitudinal and spatial data, estimating molecular networks, as well as genome-enabled phenotype and genetic risk prediction methods involving high-dimensional dynamic systems in biology and medicine. The above clearly calls for the use of tailor-made mathematical modeling, artificial intelligence (AI) approaches, and Bayesian full probability modelling. Bayesian AI models can provide their predictions in terms of probability distributions showing uncertainties around the point estimates.

In high-dimensional statistical methods applied to real world problems, we have an essential focus on computational approaches to attain benefits beyond scalability including characteristic of the problem and transparency of the used data science method. We do not want to compromise the problem perspective only for computational reasons. This means, for example, that we want to consider settings with fully utilizing the data collection architecture in computation so that as much as possible computations are done locally, and only sufficient statistics are transmitted globally. This corresponds to settings that are beneficial to understanding and using transparent prediction methods, rather than “black box” predictors.

From a biological perspective, the group studies different problems in QTL mapping and genomic prediction. Especially, the properties and use of Bayesian multi-locus models (i.e., Bayesian whole-genome regression or Bayesian alphabets) in identifying genetic determinants (including epistasis) and prediction of individual's genetic value (merit / risk) of quantitative, qualitative and function-valued traits in humans, plants and animals based on genome-wide sets of molecular markers. In addition, the group have a specific interest towards use of Bayesian mixed models such as Bayesian G-BLUP and other mixed models in estimating genomic parameters, genomic heritability or genomic breeding values.

Newer interests are different variance and precision matrix inference methods having sparsity-inducing mechanisms. The conditional independence structure between variables is equivalent to estimating the topology of the graph or estimating the network structure. Thus, we are developing new statistical sparsity-inducing estimation tools for variance and precision matrices as well as estimating network structures from molecular data sets. Biologically this is closely connected to different problems in systems biology.

Machine learning (ML) has become an important part of AI and many of the recent approaches in our group fall within the ML framework. Of particular interest are proximal gradient descent (e.g. various LASSO approaches), Bayesian additive regression trees, approximate Bayesian neural networks, convolutional and graph neural networks for genomic prediction. As a complement to these approaches, we have also developed novel methods for explainable AI (XAI) in deep learning models applied to genomic data as well as for medical image diagnosis.

Key words: quantitative genetics, machine learning, AI

• Head: Eeva Sliz, Adjunct professor
• Faculty: Faculty of Medicine, Research Unit of Population Health
• Group composition: 1 Professor, 2 Adjunct professors, 2 Postdoctoral researchers, 8 PhD students, 4 Master’s students
• Websites: https://www.oulu.fi/en/researchers/eeva-sliz
• Contact: eeva.sliz (at) oulu.fi

Inflammation, a fundamental biological response to infection, injury, and other harmful stimuli, plays a crucial role in human health. While short-term, acute inflammation is a protective mechanism, chronic or dysregulated inflammation has been associated with a wide range of diseases, including autoimmune disorders, cardiovascular diseases, and cancers, among others. Understanding how genetic factors influence inflammatory processes and how these processes contribute to disease risk is essential for improving prevention, diagnosis, and treatment strategies. In recent years, the availability of large-scale biobank data has provided unprecedented opportunities to explore the genetic and other determinants of inflammation and their impact on human disease. FinnGen is a Finnish biobank-collaborative, and among the most appreciated sources for health research. FinnGen and other biobanks, such as UK Biobank and Estonian Biobank, contain detailed genetic and phenotypic data from hundreds of thousands of individuals, making it possible to conduct comprehensive studies on inflammation-related pathways and their associations with various diseases.

The overarching goal of this project is to investigate the role of inflammation in modulating disease risk using large biobank data. Specifically, we aim to study how genetic modulators of inflammatory processes alter the risk of human disease. By leveraging biobank data, we will assess not only the direct genetic associations with inflammatory component-containing diseases but evaluate also gene-gene interactions that may exacerbate disease risk. This project holds significant potential for advancing our understanding of the complex interplay between inflammation, genetic factors, and disease. The insights gained could pave the way for novel therapeutic interventions aimed at targeting inflammatory pathways and improve patient stratification to better identify high-risk patients. Ultimately, the findings may contribute to reducing the burden of inflammation-related diseases and improving public health outcomes globally.

We conduct research in a dynamic, collaborative environment with a strong emphasis on genetic epidemiology using large-scale data. Our team has top-notch publications in genomics and multiomics (e.g., PMIDs 38448586, 36726022, 30524137) and inflammation-related human diseases (PMIDs 37453363, 34454985, 31217265). If you are interested in exploring biobank data to uncover the genetic underpinnings of inflammatory pathways influencing disease risk, please do not hesitate to contact us directly to discuss potential ideas and opportunities.

Key words: genetics; inflammation; gene-gene interactions; genetic epidemiology; human disease

• Head: Eija Tanskanen, Professor, director
• UOULU Unit: Sodankylä Geophysical observatory (SGO)
• Group composition: Seppo Vainio, Tuukka Petäjä, Markku Kulmala, Henrikki Liimatainen, Feby Pratiwi, Marko Suokas, Soile Jokipii-Lukkari, Juha Röning
• Website: https://www.sgo.fi
• Contact: eija.tanskanen (at) oulu.fi

Outdoor air pollution is globally a major premature mortality factor while in reverse pure air in natural environments is known to provide human components that promote human health. Identification of the physiochemical forces and mechanisms by which such exposomic impact is mediated to regulate human health is of critical importance when considering changing atmosphere, air pollutants and species diversity decay.

Particles from man-made and natural sources vary the content and dynamics of the atmosphere. Thunderstorms, meteoroid dust and geomagnetic storms carry new cosmic material to the different atmospheric layers, and furthermore to the surface of the Earth. This material modulates atmospheric processes and human activities in space and ground as well as affect to the health of humans.

By solving the atmospheric health exposure mechanisms would offer evident grounds for novel therapeutic for preventive measures and thus critical medical tools for interventions of allergies but also premature death by particulate exposure.

We have gained novel data on how the environment-genome interaction dynamics may operate to impact human health by involving a robust molecular genetically loaded universal species excreted bioaerosol type nanobodies (BAs) serve as epigenetic exposed regulators. These biosynthetic BAs and such ecosystem nanostructures transfer wealth of epigenetic molecular regulators and may serve also as novel cosmic radiation and pollutant impact carriers and provide at the end lung cell entry to the human body via a virus bioaerosol (BAs)-like mimicry based mechanism.

In this post doc project, we will take use of the Finnish atmospheric SMEAR stations and the SGO real time environmental data and measured physical BA particulate matter. The data will be set in relation of the data derived from the impactor bio- and databank that have delivered atmospheric molecular OMICS data. The BA data sets can be expected also to offer access also to characterize the species diversity composition based on meta bar-coding tools in the nature derived nanobodies and correlated to publicly available geographic location data and the retrospective atmospheric data to be analyzed against such data clusters. The chemical, physical and location datasets will be further aimed to be correlated to inhabitant location, human health registry and open access national genome data such as the Finngen in aims to identify the degree of the exposomic data to that of the human registry and cohort data of the Finnish citizens.

Key words: atmospheric particles, space forcing/space radiation, human health

• Head: Kaisa Tasanen-Määttä, Professor and Laura Huilaja, Docent
• Faculty: Faculty of Medicine
• Group composition: 1 professor, 5 post-docs, 5 PhD students
• Website: http://www.oulu.fi/mrc/research-groups/tasanen
• Contact: kaisa.tasanen (at) oulu.fi

Skin diseases and conditions are among the most frequent reasons for consultation in primary care. Although some are self-limiting or can be treated with over-the-counter medications, some require multiple visits, are associated with other diseases leading to multimorbidity or even lead to heightened risk of mortality. To fully understand these associations at population level, an epidemiological approach is needed. Another major aim is to add knowledge of the genetic background of skin diseases which may lead to development of new therapeutic strategies.

To understand better the life-course determinants and pathways to skin diseases we have performed full dermatologic status investigation of 1932 members of the Northern Finland Birth Cohort 1966 (NFBC 1966). NFBC 1966 is a longitudinal research program which included initially all 12058 children whose expected time of delivery was in the year 1966. The whole NFBC 1966 cohort has been followed on a regular basis since antenatal period by health care records, questionnaires and clinical examinations as well as data on their parents and offspring. During a 46-year follow-up survey, 1932 cohort members were evaluated by multidisciplinary examination including body index, blood pressure, heart echo recording, dental status and several other health measurements.To broaden the perspective to older age-group, the skin examination was also performed to parents of NFCB1966 (N=700).

Another source for epidemiological data is the national registries. The Finnish Care Register for Health Care (CRHC), maintained by Finnish Institute of Health and Welfare, is one of the longest-standing individual-level hospital discharge registries. It contains data from both hospitalized patients and those treated in the outpatient clinic of all hospitals across Finland. CRHC contains personal and hospital identification codes, data on age, gender, length of stay and subsidiary diagnoses. Data about all reimbursed medications purchased in Finland can also be found from national registry (KELA). By combining these registry data, we can study in nationwide-level the medication use and comorbidities in several skin diseases. Currently, we have built up cohorts, in which we study comorbidities in atopic dermatitis, bullous pemphigoid, basal cell carcinoma and hand eczemas.

In addition, we work as part of FinnGen-project. The FinnGen Research Project (https://www.finngen.fi/en) was designed to establish a resource that combines data from the Finnish national registers (CRHC, KELA) with genome-wide variant data from 500 000 Finns. The premise of the project is based on Finland’s high degree of genetic homogeneity, which is due to the country’s unique population history. This makes it easier to identify novel risk alleles in a relatively homogeneous population than it would be more difficult to find in a more genetically diverse one. In collaboration with professor Johannes Kettunen and his research group we have performed genome-wide association studies (GWAS) in atopic dermatitis and psoriasis. We have ongoing GWAS projects in seborrheic dermatitis and skin cancers.

Key words: skin diseases, genetics, national health registries

• Head: Outi Uimari, MD PhD Docent
• Faculty: Faculty of Medicine
• Group composition: 1 professor, 1 docent, 3 postdocs, 2 researchers
• Website: https://www.oulu.fi/en/research-groups/endometriosis-and-uterine-leiomyoma-research-group
• Contact: outi.uimari (at) oulu.fi

The diagnostic challenge in endometriosis is multi-faceted and the definitive diagnosis currently requires surgery. Herein we present a pursuit to develop a novel non-invasive method to diagnose endometriosis with skin patch, which is a new method for collecting, extracting and analysing exosomes from sweat.

Endometriosis is a common gynecological disease affecting 6-10% of female population world-wide. It causes chronic pain and infertility. At present the biological and causal mechanisms that lead to endometriosis remain poorly understood. The diagnostic challenge in endometriosis is multi-faceted. Symptoms such as dysmenorrhea, dyspareunia, dyschezia, and infertility can be attributed to many conditions and the definitive diagnosis currently requires surgical visualization during laparoscopy.

Exosomes are extracellular vesicles generated by all cells and they carry nucleic acids, proteins, lipids, and metabolites. They provide new mechanism by which cells indeed communicate between each other. Given the fact that exosomes contain the wealth of molecules, they offer elegant ways to identify the disease related molecular signatures. These signatures can be identified in purified exosomes detected in all body fluids including plasma/serum, sweat, endometrium, and peritoneal fluid. Wnt signaling, which plays a pivotal role in endometriosis pathogenesis, to communicate through exosomes by multiple molecules. Exosome cargo contains different miRNAs with regulatory properties influencing Wnt pathways. Technology is availbale for collecting, extracting and analysing exosomes from sweat.

Exosomes may hold significant potential as biomarkers or therapeutic targets to improve the outcomes of women affected by endometriosis, particularly peritoneal disease that currently and most often requires surgical confirmation for diagnosis and where surgical excision of lesions very rarely offer symptom relief. On that account, our aim is to establish a new research venue at Oulu University to characterize exosomes of endometriosis patients’ plasma/serum, sweat, endometrium and peritoneal fluid and explore whether these molecular changes in Wnt signaling can be observed in comparison to endometriosis-free women.

This EU COFUND Data4health project will be focused on (1) exploring the genetic signature of endometriosis subtypes (peritoneal, ovarian, deep) using data arising from FinnGen with ~21,000 endometriosis cases and ~270,000 female controls, (2) collecting, extracting and analyzing exosomes from sweat through skin patch by setting up a biobank re-call study aiming at 100 endometriosis cases and 100 controls, and (3) testing in practice whether exosomes offer a novel approach to endometriosis diagnosis and treatment with recruitment of endometriosis patients having surgery at Oulu University Hospital.

Key words: endometriosis, extracellular vesicle

• Head: Johanna Uusimaa, MD, PhD, Professor of Pediatric Neurology
• Fatulty: Faculty of Medicine, Research Unit of Clinical Medicine and Medical Research Center, Oulu University Hospital and University of Oulu
• Group composition: 2 professors, 4 senior researchers, 4 post docs, 10 PhD students, 1 laboratory technician
• Website: https://www.oulu.fi/en/research-groups/pediatric-neurology-group
• Contact: johanna.uusimaa (at) oulu.fi

Globally, neurological diseases are increasingly impairing functional capacity and quality of life, underlining the importance of advanced diagnostics and target discovery for preventive or disease-modifying treatments. Data4BrainHealth is a clinical and translational project focused on neurological diseases affecting children, especially childhood-onset movement disorders, epilepsy, and white matter diseases. The Data4BrainHealth project will provide a deeper understanding of genes, protein networks, and signalling pathways in human brain development to tackle challenges in the diagnostics and treatment of diseases involving the developing brain.

Childhood-onset brain diseases affect brain health across the lifespan and have significant individual and societal implications. Thereby, the three Data4BrainHealth aims are: 1) identifying new genetic aetiologies and biomarkers in childhood-onset brain disorders to improve diagnostics and disease monitoring; 2) target discovery, including shared disease mechanisms to tackle multiple rare disorders, and 3) a systems-level approach utilizing patient-derived induced pluripotent stem cells (PD-iPSCs), organoid models, human brain tissue samples and publicly available large datasets.

The Data4BrainHealth project is based in the Research Unit of Clinical Medicine at the University of Oulu and the Pediatric Neurology Unit of the Oulu University Hospital, Finland. The multidisciplinary team is led by principal investigator, Professor Johanna Uusimaa, MD, PhD, Head of the Pediatric Neurology Unit at the Oulu University Hospital, and 3 post-doctoral researchers: Salla Kangas, PhD (patient-derived cellular models, cell biology, biochemistry, proteomics, transcriptomics), Jussi-Pekka Tolonen, MD, PhD (diagnostics of childhood-onset neurological diseases, iPSC methodology, brain organoids), and Esa-Ville Immonen, PhD (cellular electrophysiology, neurobiophysics, ion channels). Professor Uusimaa has identified and characterized several previously unknown inherited human diseases and established active national and international research networks representing the pediatric neurology unit of the Oulu University Hospital and Northern Ostrobothnia Hospital District as a full member of European networks such as the ERN-RND (Rare Neurological Diseases) and ERN-NMD (Neuromuscular Diseases) Finland consortia, ERN-EpiCARE Helsinki-Oulu, and ERN-ITHACA networks.

To study disease mechanisms in childhood-onset neurological diseases, the Data4BrainHealth project utilizes 1) clinical data on patient cohorts; 2) neuroradiological and neurophysiological data; and 3) patient-derived cellular and organoid disease models, and brain tissue samples. The successful recruit will focus on the bioinformatic analyses of diagnostic next-generation sequencing studies, and transcriptomics, proteomics, and metabolomics datasets generated from tissue-specific disease models, involving PD-iPSC neuronal cultures and brain organoids. Importantly, our laboratory has developed methodology to extract human brain tissue samples from medical instruments used in epilepsy surgery and implantation of deep brain stimulation devices – the successful recruit will take part in the analysis of these unique datasets.

Finally, neurological diseases often manifest as dysfunctional electrochemical activity within neural networks. For instance, epilepsy is characterized by the hyperexcitation of cortical pyramidal neurons, often leading to excitotoxicity and cell death. To study the functional aspects of patient-derived disease models, we will use single-cell (perforated and whole-cell patch-clamp) and small-network (local field potentials and spike patterns using commercially available multielectrode arrays) level electrophysiology. Our aim is to reveal abnormalities in ion channel expression/function, synaptic signalling, and functional connectivity.

Key words: Multiomics, iPS-based disease modelling, Neurology, Neurodevelopment

• Head: Seppo Vainio, Professor
• Faculty: Faculty of Biochemisty and Molecular Medicine
• Group composition: Seppo Vainio, Arto Mannermaa, Anna Hyvärinen, Anatoliy Samoylenko, Erfan Khamespanah, Juha Röning, Outi Uimari, Tuija Männistö
• Website: https://www.oulu.fi/en/research-groups/developmental-biology-laboratory-organogenesis-extracellular-vesicles
• Contact: seppo.vainio (at) oulu.fi

The incidence of cancer is growing due to population aging and exposure to still poorly characterized environmental load. Cancer to emergence needs one or multiple genomic hits. Cancer represents not a single disease, but several types of manifestations associate to the array of disease symptoms and cell types with different molecular make up. Thus, better understanding of the genetic foundations is needed to obtain efficient diagnostic and therapeutic. Of relevance here is that when the mutational load occurs on the cell division, apoptosis and genomic proof-reading genes the risk for transformation become higher. It is also worth noting that cancer arise via those genes that control normal development in ontogenesis. For example, genes that are behind metastasis represent those that control of cell migration during gastrulation to establish the neural crest derivatives.

Identification of the liquid biopsy a valuable diagnostic opening and that this medium also integrated the cell secreted nano- and micro size extracellular vesicles (EVs). This provides a tremendous opening to advance our understanding of mechanism in initiation and spread of cancer. EV based mechanisms are connected for example to induction breast cancer. We hypothesize for this post-doctoral program that the gut ecosystem may indeed secrete initially toxic EVs that transcytoses across the gut. Such as mechanism may then go on to transmit such genotoxins systemically to target cells such as the breast to induce genomic alterations. Such insults that will accumulate during the life cycle may become possibly redistributed similarly by an EV based transmission mechanism to the germ line. This potential is based on the noted capacity of EVs to cross various biological including the germ line in their reproductive system providing a working hypothesis for a disease burden.

To save lives the earlier transformed cancer initiating stem cells can be diagnosed the higher likelihood is for patient survival. It is relevant from the diagnostic point of view that once tumorigenic cells have arisen a risk for relapse remains. Given the EVs as complex cell secretome signals they provide excellent opportunity for early-stage cancer diagnostics and foundations for novel therapeutic. EVs can also carry tumour specific molecular signature and EVs can be loaded with small molecules in aims for tumour cell eradication. By analysing EV based data sets from excreted body fluids and by conducting of experimental EV based interventions EVs can be expected to provide means to develop non-invasive, early-stage cancer diagnostics but also diagnostics of the therapeutic efficacy.

In this post-doctoral project, we will use generated blood based cancer associated systemic genomic liquid biopsy and cohorts derived genomic data sets. These will be used to address if in a model clinical cancer set up such severity associated diagnostic biomarkers can be depicted also non-invasively. These studies are aimed to be conducted on patient data derived before and after the surgical intervention. Moreover, the aim is to use a biobank database to obtain a robust biomarker pool to be coupled to the cancer patient registries and public domain data analysis. The EV and genomic informatics serve to enable bioengineering EVs for model tumour targeting towards novel therapeutic and its efficacy diagnostic.

Key words: Liquid biopsy, oncosomes, relapse, biosensors

• Head: Dr. Rajaram Venkatesan, Senior Research Fellow
• Factulty: Faculty of Biochemistry and Molecular Medicine
• Group composition: Dr. Rajaram Venkatesan: 3 PhDs & 4 PhD students & Prof. Seppo Vainio3 seniors, 4 PhDs and 3 PhD students
• Websites: Dr. Venkatesan: https://www.oulu.fi/en/research-groups/venkatesan-research-group
• Prof. Vainio: https://www.oulu.fi/en/research-groups/developmental-biology-laboratory-organogenesis-extracellular-vesicles;
• Contact: rajaram.venkatesan (at) oulu.fi

Tuberculosis (Tb) is an airborne infectious disease caused by the bacterium Mycobacterium tuberculosis (Mtb). If not treated can be fatal. About 10 million become sick with Tb leading to more than a million deaths annually. Moreover, approximately, one-fourth of the world population is infected with the latent form of tuberculosis. In addition, drug-resistant strains are emerging all the time leading to more complications. Therefore, there is great need to develop early-stage diagnostic and therapeutic strategies against Tb.

Exosomes are molecularly loaded lipid enriched vesicles naturally secreted also by mycobacteria. Vainio and Venkatesan group have joined forces to explore the possibility of using mycobacterial derived and also mammalian engineered exosome as tools for the diagnosis and treatment of this deadly disease Tb.

The EU COFUND post-doctoral project will be focused on generation via gene editing engineer cell secreted nanovesicle exosomes to be used as novel Advanced Medical Therapy products (ATMPs).

The selection of the bio druggable compounds is based on the Mtb bioinformatic and structural data analysis to identify candidate antigens to be genetically engineered to be displayed in the therapeutic exosomes in cell factories. Such antigen presenting cell will be thus also generated and applied. The aim in the project is to use the depicted experimental medicine approach to develop novel noninvasive diagnostic measures for the Tb.

Key words: Mycobacterial exosomes, tuberculosis, multiomics

• Head: Pirjo Åström, Academy Research Fellow
• Co-PI: Mikko Finnilä Academy Research Fellow
• Faculty: Faculty of Medicine, Research Unit of Biomedicine and Internal Medicine
• Group composition: Principal investigators (senior researchers): 2, postdocs: 3, PhD students: 8
• Websites: https://www.oulu.fi/en/researchers/pirjo-astrom, https://www.oulu.fi/en/researchers/mikko-finnila
• Contact: pirjo.astrom (at) oulu.fi

It is not well known how viruses that enter neurons are related to common, high burden chronic diseases. Our group aims to understand the mechanisms of inflammation and the association of viral infections in chronic disease processes. More specifically, we are interested in the connections between common alpha herpes virus infections with arthritic diseases and dementia.

Human alpha herpes viruses (α-HVs), including herpes simplex viruses (HSV) establish lifelong latency in neurons of the peripheral nervous system. Thus, the infected individuals remain lifelong carriers of the virus. Although α -HVs mainly stay dormant in peripheral neurons, they may also enter central nervous system (CNS) with mild symptoms or, in rare cases, cause severe conditions, including encephalitis.

There is increasing evidence on the connection of α-HV infection and dementia such as Alzheimer's disease. Also, an epidemiological association between α-HVs and neurodegenerative diseases has been found. Interestingly, an association of HSV2 infection and rheumatoid arthritis has also been reported. Whether those individuals with re-activating virus, shown as rather harmless epithelial blisters, are at greater risk for virus entry to CNS, or for developing arthritic diseases and dementia is unknown. The underlying mechanisms on how these viruses and neuroinflammation in general affect chronic disease processes are poorly understood.

As part of our group, you would be contributing to provide increased understanding on the effects of viral infections on health and disease, including dementia and arthritic diseases. The study also aims to offer insights into novel defense mechanisms protecting CNS from viruses as well as neuroinflammatory mechanisms contributing to disease processes and symptoms, including pain. The knowledge on the connections between viral latency and reactivation on various diseases and the underlying mechanisms could allow prediction of future health and potential novel therapeutic approaches to disease prevention and treatment.

The project will be carried out in the Faculty of Medicine, University of Oulu. The research group PIs are both part of the Biocenter Oulu (BCO) Multidisciplinary Center as Emerging project leaders. BCO core facilities, include imaging, FACS, proteomics, sequencing and transgenic core at the campus. The group has collaborative partners in Europe and North America, which provide support in terms of advanced methodology and expertise. We utilize Finngen database and Northern Finland birth cohort to reveal connections between viral infections with arthritic diseases and dementia. Within the project, novel humanized in vitro models are developed to study cellular events and interactions related to mechanisms of viral defense, neuroinflammation and pain. In addition, you would have access to mouse models to examine the mechanisms involved in defense against viruses and the role of inflammation in disease processes and symptoms, including aberrant inflammation and pain. We utilize proteomics, metabolomics and transcriptomics methods for biological samples in conjunction with immunohistochemical and imaging data from animal experiments and various data from cohort subjects.

A successful applicant should be interested in big data approaches to reveal potential novel associations and to validate results with experimental models to provide mechanistic insights on etiological processes.

Key words: alpha-herpes viruses, arthritic diseases, dementia, disease mechanisms, framework for multimodal data analysis