Dissertation: May 5th, at 13:15-15:30, 2023
Location: Høyteknologisenteret, lille auditorium
Title of thesis: “A matter of timing: A modelling-based investigation of the dynamic behaviour of reproductive hormones in girls and women”.
Summary of thesis:
The hypothalamic-pituitary-gonadal axis (HPG axis), a part of the human endocrine system, regulates the female reproductive function. Feedback interactions between hormones secreted from the glands forming the HPG axis are essential for establishing a regular menstrual cycle. Mathematical models predicting the time evolution of hormone concentrations and the maturation of ovarian follicles are useful tools for understanding the dynamic behaviour of the menstrual cycle. Such models can, for example, help us to investigate pathological conditions, such as endometriosis or Polycystic Ovary Syndrome. Furthermore, they can be used to systematically study the effects of drugs on the endocrine system. In doing so, menstrual cycle models could potentially be integrated into clinical routines as clinical decision support systems. For the simulation-based investigation of hormonal treatments aiming to stimulate the growth of ovarian follicles (Controlled Ovarian Stimulation (COS)), we need models that predict hormone concentrations and the maturation of ovarian follicles in biological units throughout consecutive cycles. Here, I propose such a mechanistic menstrual cycle model. I also demonstrate its capability to predict the outcome of COS. Individual time series data is usually used to calibrate mechanistic models having clinical implications. Collecting these data, however, is time consuming and requires a high commitment from study participants. Therefore, integrating different data sets into the model calibration process is of interest. One type of data that is often more feasible to collect than individual time series is cross sectional data. As part of my thesis, I developed a workflow based on Bayesian updating to integrate cross-sectional data into the model calibration process. I demonstrate the workflow using a mechanistic model describing the time evolution of reproductive hormones during puberty in girls. Exemplary, I show that a model calibrated with cross-sectional data can be used to predict individual dynamics after updating a subset of model parameters. In addition to the scientific contributions of this thesis, I hope that it creates attention for the importance of research in the area of women’s reproductive health and underpins the value of mathematical modelling for this field.
Inflammatory bowel disease (IBD) is a complex disorder that involves chronic inflammation of the digestive tract. IBD has mainly two subtypes, Crohn’s disease (CD) and ulcerative colitis (UC). IBD affects around 6.8 million globally, and < 1.3 million people in Europe. IBD can be devastating, resulting in significantly reduced quality of life.
There are multiple factors, such as genetics, environmental factors, microbiome, and immune system that cause IBD; but the molecular mechanisms behind IBD are still elusive. A significant proportion of patient fails to respond to therapy. Therefore, it is important to identify non-responders to standard therapy, so alternative treatments can be applied sooner.
In this work, a biological interpretable quantitative model was developed that predicts therapy response in UC patients. This model characterizes the individuals based on how strongly they respond to their immune systems’ alarm signals. These signals are interpreted through a complex network of protein molecules that determine how cells respond. We propose a mathematical model of how these networks process signals differently in each individual and relate this to which drugs patients respond to.
Place: Høyteknologisenteret, Stort auditorium (Big auditorium)
Pål Vegard Johnsen
Dissertation: 21th January
PhD thesis title:
Samaneh Abolpour Mofrad
In this thesis I investigate the role of Golgi fragmentation in breast cancer cell migration and progression using an integrated experimental, quantitative and computational approach. The outcome of this thesis is four computational tools: DiscBIO, CellMAPtracer, cellmigRation and the mathematical modeling, in addition to highlighting potential roles for Golgi fragmentation in breast cancer progression. I consider this outcome as a starting point for further investigations.
Altogether, the results of this thesis point out the importance of cell migration for tumor progression and identify Giantin as a potential biomarker for cancer progression opening new doors with many directions for validating and further investigating the main findings.
The health of the body and the brain are intrinsically connected, with the structure and function of the ageing human brain being vulnerable to the effects of poor cardiovascular health. Cardio- vascular and -metabolic risk factors such as smoking, dyslipidemia, high blood pressure, obesity, and markers of inflammation are associated with an increased risk of neurocognitive conditions such as dementia and stroke, in addition to a range of mental disorders and age-related cognitive decline.
While poor cardiometabolic health may negatively impact brain health, strategies that promote cardiometabolic health may conversely halt the onset of age-related pathological changes in the brain. Increasing knowledge about the mechanisms of cardiometabolic risk factors (CMRs) and their association with brain structure and integrity is necessary for the development of treatment strategies that delay ageing-related neurodegeneration.
In the current thesis, we used a combination of cross-sectional and longitudinal datasets to investigate brain and cardiometabolic health. Utilising brain age prediction based on neuroimaging data (Franke, Ziegler, Kloppel, Gaser, & Alzheimer’s Disease Neuroimaging, 2010), we first tested the age prediction accuracy of brain age models based on diffusion magnetic resonance imaging (MRI) metrics, followed by investigating brain age gap (BAG, the difference between the brain-predicted age and chronological age) associations with CMRs, including clinical measures, blood test measures, and measures of adipose tissue distribution from body MRI.
John Zobolas at NTNU
Summary of thesis:
Cancer is one of the leading causes of death globally. To combat cancer, scientists want to understand the mechanisms that drive this disease so that effective treatments can be developed. However, regulatory mechanisms of cancer can be very complex, so its analysis has proven to be quite challenging. The field of Systems Medicine has emerged to address this problem, combining expertise from different scientific disciplines such as Biology, Medicine and Computer Sciences. This approach aims to integrate, analyze and interpret data from different resources and produce new knowledge, which is the basis for the development of personalized therapies. The efforts described in this PhD thesis have improved various parts of a systems medicine approach towards cancer therapy development.
We developed software tools that enable a better collection and sharing of biological knowledge described in scientific publications. Our software facilitates the task to search and annotate information about molecules and their interactions and to store it in a format computers can understand. With this knowledge scientists can build networks describing the structure and signaling information of biological systems such as the human cell. These signaling networks can be used to construct computational cancer models, which can explain why signals traveling through these networks get disrupted, leading to disease. We implemented simulation software that automatically builds and optimizes many models to match given cancer signaling data. Our computer models successfully predicted drug combinations that act synergistically in experiments. A new software tool was built to analyze the simulation data and find biological or modeling markers that explain why some drug combinations are beneficial while others are not. Lastly, we proposed a mathematical framework that allows us to optimally set the parameters of our models and make them better match experimental observations and comply with the underlying signaling information.
Einar Marius Hjellestad Martinsen
Summary of thesis:
Antibodies and B cell receptors are crucial components of the immune system. However, immunoglobulin genes, which encode both antibodies and B cell receptors, are not well explored. This is partly due to high similarities among immunoglobulin genes, large structural variation of the immunoglobulin loci as well as the presence of somatic hypermutation in antigen-experienced B cells.
In this thesis, we used a dataset composed of naïve immunoglobulin repertoires from a cohort of 100 Norwegians. Since naïve B cells have not undergone somatic hypermutation, we were able to infer germline alleles from this dataset. We optimised existing software tools for germline inference and discovered a great amount of structural variation and a large number of previously unreported immunoglobulin V alleles in both heavy and light chain genes. On top of that, we developed an approach for filtering potential sequencing and PCR artefacts.
We also analysed the leader sequences and 5’ untranslated regions (5’UTR) of immunoglobulin genes and discovered even more polymorphisms in these regions. Surprisingly, we found several sequences with identical coding V region that had different 5’ UTRs and/or leader sequences. Our analysis also revealed alternatively spliced transcripts of genes with low usage levels, which raises questions about mechanisms that regulate the expression of immunoglobulin genes.
The results of our work provide a valuable contribution to the efforts to characterise germline immunoglobulin gene variants. This will enable further research into the functional effect of immunoglobulin polymorphisms as well as exploration of possible influence of coding and non-coding immunoglobulin polymorphisms on health and disease.
Summary of thesis:
Celiac disease is a chronic inflammatory disorder resulting from mis-appropriate immune response toward ingested gluten proteins. Gluten-specific CD4+ T cells, as the key drivers for the pathogenesis can be identified by staining with HLA-DQ:gluten tetramers.
In this project, we conducted single cell transcriptome sequencing on CD4+ T cells sampled from peripheral blood of untreated CD patients. The tetramer-specific T cells showed transcriptomic profiles consistent with activated effector memory T cells that share features with Th1 and follicular helper T cells. Compared to non-specific cells, gluten-specific T cells showed differential expression of several genes involved in metabolic processes, including fatty acid metabolism and redox potentials. In addition, we utilized the unique expression of immune receptor of each T and B cell to quantify the impact of index switching on single cell RNA-seq experiments.
We also demonstrated that the state of celiac disease could be inferred by unbiased direct TCR sequencing on lamina propria T cells, which is promising for the ultimate goal of inferring celiac disease state based on TCR sequencing of circulating T cells.
Vanessa Carina Bieker
Summary of thesis:
The world’s herbarium collections contain a vast number of specimens that were collected up to 400 years ago. Due to recent advances in DNA extraction and sequencing, these specimens are now readily available for use in genomic studies. This allows us to directly sample past diversity and reveal evolutionary and population histories that are difficult or even impossible to infer from modern data alone.
Due to globalization, increasing numbers of species are introduced into locations they could not reach through natural dispersal. Some of these introduced species are able to establish a stable population in the introduced range and eventually become invasive. These species may be able to outcompete native species and thus can drive them to local or even global extinction. Therefore, invasive species are a threat to global biodiversity. Despite this, invasive species are good study systems for evolutionary processes. Each introduction to a new environment can be viewed as a natural experiment that is often running for more generations than can be studied e.g. in experimental evolution studies. Thus, parallel adaptation to similar environments can be studied. In combination with archaeobotanical samples or historic herbarium records, these changes can be observed directly using genetic evidence. One hypothesis as to why invasive species are successful in the introduced range is the Evolution of Increased Competitive Ability (EICA) hypothesis. It says that due to the release from native enemies, plants are able to allocate resources away from defense mechanisms and towards increased growth and reproduction which makes them better invaders.
Ambrosia artemisiifolia (common ragweed) is a very successful invasive annual weed native to North America that was introduced to Europe in the late 19th century. In this thesis, I investigate the genomic basis of this invasion using 297 historic herbarium specimens and 350 contemporary samples from both the native and the invasive range in what is thus far the largest study of whole genomes of a non-model, non-crop plant. I found that the population structure in the native range contains three main genetic clusters and one admixed cluster, and, using this information, I was able to identify the most likely source population for the European invasion. Unlike in the native range, population structure changed drastically over time in Europe. Moreover, I found selection on traits in Europe which are consistent with the EICA hypothesis. In addition, I used the herbarium specimens as well as the contemporary samples in the first study of its kind to look at differences of the metagenomic community between ranges and over time. I found that some taxa are less common in Europe, providing evidence that enemy release might have played a role in the plants’ invasive success. This thesis demonstrates how genomic data from herbarium specimens can be used in a variety of studies and how they add value to the study of invasion.
Therapeutic efficacies for age-related diseases such as heart failure, diabetes and sarcopenia are inadequate because heart failure is still the main cause of death worldwide, prevalence of diabetes is increasing, and, there are no therapeutic options to combat sarcopenia. Therefore, innovative approaches to contest these age-related diseases are critically needed. This thesis explored exercise-mechanisms and the noncoding genome to identify novel genes implicated in age-related diseases with clear therapeutic potential.
A large body of evidence indicates exercise as an undisputed mediator of cardiac/skeletal muscle health. However, molecular mechanisms underlying the benefits of exercise are not fully understood. Hence, harnessing exercise-mechanisms might present an intriguing strategy to therapeutically deliver some specific benefits of exercise.
An additional treasure trove for gene target discovery opened up when the human genome was sequenced in 2003, shockingly showing that 99% of DNA is noncoding. The noncoding part of DNA is not well studied and has therefore been termed the “dark genome”. The dark side of the human genome partly consists of enhancer elements, which contain the vast majority of genetic variants associated with common diseases. Another intriguing entity comprised in this unknown part of our genome are large transcribed, but untranslated genes called long noncoding RNAs (lncRNA). First disregarded as cellular byproducts, functional lncRNAs affecting whole-body traits have been reported recently.
Given the intriguing potential of exercise (Review Paper) and the noncoding part of the human genome for gene-target discovery in age-related diseases, the goal of this thesis was: to test whether exercise alters expression of genes in heart failure, which could be harnessed to protect the failing heart from hypoxia (Paper I); to identify metabolic enhancer elements that alter activity upon diabetes-induction and harbor genetic variants associated with metabolism (Paper II); and finally, to dissect the intersection of exercise and the noncoding genome by searching for an exercise-altered lncRNA in skeletal muscle and characterizing such a lncRNA in muscle ageing (Paper III).
In Paper I, we discover PRODH expression to be reduced by heart-failure and rescued by exercise. PRODH is indispensable for maintaining mitochondrial bioenergetics and ATP levels in a hypoxic environment, while reconstitution of PRODH attenuates hypoxic impairments. In Paper II, we map skeletal muscle enhancer elements that alter activity upon diabetes induction and harbor single nucleotide polymorphisms associated with metabolic traits. Assessing physical interaction and transcriptional regulation between these enhancers and their target genes reveals several candidate genes whose expression correlate with whole-body metabolic traits. In Paper III, exercise-altered lncRNAs in skeletal muscle are identified. In worms, mice and humans the exercise-induced, pro-myogenic lncRNA CYTOR improves aged muscle function by reversing the age-associated loss of fast-twitch, type II muscle fibers. In conclusion, harnessing exercise and the noncoding genome for novel gene target discovery identified several promising gene targets implicated in the age-related diseases heart failure, diabetes and sarcopenia.
Bjørn Bredesen at University of Bergen
Dissertation: November 20th at 10.15-12.00
Title of thesis: “Modelling the structure, function and evolution of Polycomb/Trithorax Response Elements”
External link to dissertation here.
Sonja Lagström at University of Oslo
Persistent infection with a high-risk human papillomavirus (HPV) type is necessary for cervical cancer development, causing nearly 5% of all cancers worldwide. Nevertheless, only a small fraction of HPV infections progress to cancer, indicating that additional molecular factors contribute to the development of cervical cancer.
The thesis aimed to characterise and explore mutations in the HPV genome and viral integrations into the human genome contributing to HPV-induced carcinogenesis. This can reveal new insight into cervical cancer development.
A unique next-generation sequencing protocol, TaME-seq, was developed for analysis of HPV genomic variation and integration. The results show that the overall intra-host HPV genomic variability is higher than previously assumed, with a high number of HPV genome variants found in all samples from early infections to cancer. A noticeable part of the mutations in HPV16, which is the most carcinogenic HPV type, was associated with the APOBEC3-enzyme that is suggested to be involved in viral clearance. The findings revealed integration sites that located both in previously reported and novel genomic sites. A large number of integrations was observed in or close to human cancer-related genes, which could be an indication of a more aggressive infection.
The TaME-seq method could potentially be a valuable method for assessing the risk of developing cervical cancer. An additional HPV screening test would enable more personalised follow-up, improving detection of lesions with higher risk of progression and reducing unnecessary follow-up and treatment of women with minimal risk of developing high-grade lesions or cancer.
Yaxin Xue at University of Bergen
Joseph Diab at University of Tromsø – The Arctic University
Time: October 9th 10:15-16:00
Thesis title: “The Metabolome and Lipidome of Ulcerative Colitis”
Summary of the thesis:
Inflammatory bowel disease (IBD) is a chronic, relapsing inflammatory disorder in the gastrointestinal tract affecting up to 0.5% of the population of the Western world. The two major forms of IBD, Ulcerative Colitis (UC) and Crohn’s Disease (CD), are characterized by a dysregulated immune response triggered by several genetic, microbial and environmental factors.
We preformed mass spectrometry-based metabolomic and lipidomic analysis on colon biopsies from UC patients to unravel the disease pathobiology and to identify markers for the disease outcome. We found that the alteration in lipid mediators correlates with the severity of UC. Moreover, we report potential prognostic and diagnostic markers for UC, such as very long chain ceramids and lipid mediators. Likewise, tryptophan metabolism is a key aspect of the impaired metabolism in active UC.
This work demonstrates the importance of metabolomics in IBD to identify key drivers of pathogenesis which prerequisite personalized treatment.
The trail lecture starts at 10.15, the defense starts at 12.15
Title: “Proteomics in Inflammatory Bowel Disease – Integrated multi-omics to facilitate personalized medicine in IBD”
Pharmacogenomic screens for personalized cancer therapy are the focused biomedical application in this thesis. Due to the complex relationships between targeted cancer drugs and high-dimensional genomic predictors, we have developed penalized likelihood methods and Bayesian hierarchical models to capture the complex structures in the pharmacogenomic data and to predict drug sensitivity.
The first part of the thesis proposed to address the correlations between drug sensitivity measures for multiple cancer drugs and the heterogeneity of multiple sources of genomic data in multivariate penalized likelihood methods with structured penalities. The proposed methods can improve the prediction performance of drug sensitivity. The second part of the thesis exploited Bayesian priors for the relationships between multiple drugs and relationships between drug sensitivity and the targeted pathways or genes of cancer drugs. Large pharmocogenomic screens may also include samples from multiple cancer tissue types. We employed random effects to address the sample heterogeneity in the proposed Bayesian model. The results have shown good structure recovery in the complex data and good prediction of responses by the new Bayesian models.
We congratulate NORBIS PhD member Zhi Zhao on his upcoming dissertation! Tomorrow (Friday October 9th) at 12.15 he will defend his thesis “Multivariate structured penalized and Bayesian regressions for pharmacogenomic screens” at the University of Oslo. The trial lecture starts at 10.15. Both trial lecture and thesis defense will be streamed online and all who like can join in. Thesis abstract and (external) links to trial lecture and defense are found here: