Exploring mitochondrial quality control mechanisms and mitochondria-lipid droplet interactions in cardiac cell models

Mitochondria are the main energy producing units (organelles) in cardiac cells (cardiomyocytes). Cardiomyocytes have especially high mitochondrial content due to the heart’s continuous energy-intensive pumping. Studies of heart disease (such as heart failure) indicate that mitochondrial dysfunction is central to disease progression. There are many cellular mechanisms that protect mitochondria from harm and dysfunctional mitochondria can be removed. These mechanisms are the cell´s tools for quality control of mitochondrial function. How these quality control mechanisms function in the human heart is still not fully known. The preferred substrates consumed by the adult heart for sustaining beating are fatty acids, converted to energy by the mitochondria. Fatty acids can be stored within cells in lipid droplets for controlled use. An overabundance of lipid droplets is associated with cardiomyopathy in patients with diseases such as obesity or diabetes mellitus. The cellular response to, and mechanisms for resolving, lipid droplet overabundance in cardiac cells remain poorly understood. In the works constituting this thesis, we applied rat H9c2 cardiomyoblasts and human inducible pluripotent stem cell derived cardiomyocytes as cardiac cell models. In the cardiomyoblasts, we investigated mitophagy and mitochondrial derived vesicles, constituting different mitochondrial quality controls. Furthermore, we studied lipid droplet accumulation, degradation, and interaction with mitochondria in both cell models. For these purposes we utilized different advanced microscopy techniques. Our findings reveal that mitochondria in cells with increased mitochondrial respiration display elevated activity in the targeted quality control mechanisms. Furthermore, cells engaged in increased mitochondrial respiration accumulate less lipid droplets in response to lipid loading treatments. We also detected dynamic and close interactions between mitochondria and lipid droplets. Our work provides important insights and contributes to understanding mitochondria quality control mechanisms and the role of lipid droplets in the heart.Mitokondrier er hovedprodusentene for energi i hjerteceller (kardiomyocytter). Kardiomyocytter har spesielt høyt mitokondrielt innhold på grunn av hjertets kontinuerlige energikrevende pumping. Studier av hjertesykdommer (som hjertesvikt) indikerer at mitokondriell dysfunksjon er sentral for sykdomsprogresjonen. Det finnes mange cellulære mekanismer som beskytter mitokondrier mot skade, inkludert blant disse er mekanismer for fjerning av dysfunksjonelle mitokondrier. Disse mekanismene er cellens verktøy for kvalitetskontroll av mitokondriefunksjonen. Hvordan disse kvalitetskontrollmekanismene fungerer i det menneskelige hjertet er fortsatt ikke fullstendig forstått. Det foretrukne substratet som forbrukes av det voksne hjertet for å opprettholde hjerteslag er fettsyrer som blir konvertert til energi av mitokondrier. Fettsyrer kan lagres av celler i lipiddråper for kontrollert bruk. En overflod av lipiddråper er assosiert med kardiomyopati hos pasienter med sykdommer som fedme eller diabetes mellitus. Den cellulære responsen på, og mekanismene for å løse, overflod av lipiddråper i hjerteceller er fortsatt dårlig forstått. I arbeidene som utgjør denne avhandlingen brukte vi rotte H9c2 kardiomyoblaster og menneskelige kardiomyocytter avledet fra induserbare pluripotente stamceller som hjertecellemodeller. I kardiomyoblastene undersøkte vi mitofagi og mitokondrielle vesikler, som representerer forskjellige mitokondrielle kvalitetskontrollmekanismer. Videre studerte vi lipiddråpe akkumulering, nedbrytning og interaksjon med mitokondrier i begge cellemodellene. For disse formålene brukte vi forskjellige avanserte mikroskopiteknikker. Våre funn avslører at mitokondrier i celler med økt mitokondriell respirasjon viser økt aktivitet i de undersøkte kvalitetskontrollmekanismene. Videre akkumulerer celler som er engasjert i økt mitokondriell respirasjon mindre lipiddråper som respons på lipidbelastning. Vi observerte også dynamiske og nære interaksjoner mellom mitokondrier og lipiddråper. Vårt arbeid gir viktige innsikter og bidrar til å forstå mitokondrielle kvalitetskontrollmekanismer og rollen til lipiddråper i hjertet

Product Tracing in the Norwegian Fishing Industry Supply Chain Utilizing GoQuorum Blockchain and Smart Contracts

The Norwegian fishing industry faces a significant issue of fishery crimes, with product traceability systems presenting a potential solution to counter these illegal activities. Current supply chain management in the seafood industry is vulnerable to information alterations, thereby complicating product traceability. Blockchain technology, with its unique properties, offers an interesting approach to address these challenges. Despite this, existing blockchain-based product traceability systems often fail to integrate government regulation and provide limited access to traceability data for consumers. Moreover, those providing such access often lack user-friendliness. This thesis explores if a blockchain-based product traceability system can support supply chain management, enhance consumer confidence, and enforce regulatory compliance. We conducted a review of existing literature and assessed the potential of blockchain technology to optimize supply chain management. Furthermore, a traceability system, entitled SeaChain, incorporating a permissioned blockchain, smart contracts, and governmental regulations was developed. We evaluated this system and compared it with existing systems. Our findings suggest that blockchain technology can enhance supply chain management, bolster consumer trust, and aid in mitigating fishery crimes. The research conducted provides valuable insights for improving supply chain management and contributes to future studies in this field

Scale Space Methods for Analysis of Type 2 Diabetes Patients' Blood Glucose Values

We describe how scale space methods can be used for quantitative analysis of blood glucose concentrations from type 2 diabetes patients. Blood glucose values were recorded voluntarily by the patients over one full year as part of a self-management process, where the time and frequency of the recordings are decided by the patients. This makes a unique dataset in its extent, though with a large variation in reliability of the recordings. Scale space and frequency space techniques are suited to reveal important features of unevenly sampled data, and useful for identifying medically relevant features for use both by patients as part of their self-management process, and provide useful information for physicians

Bayesian modeling and significant features exploration in wavelet power spectra

This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis

A propagation-separation approach to estimate the autocorrelation in a time-series

The paper presents an approach to estimate parameters of a local stationary AR(1) time series model by maximization of a local likelihood function. The method is based on a propagation-separation procedure that leads to data dependent weights defining the local model. Using free propagation of weights under homogeneity, the method is capable of separating the time series into intervals of approximate local stationarity. Parameters in different regions will be significantly different. Therefore the method also serves as a test for a stationary AR(1) model. The performance of the method is illustrated by applications to both synthetic data and real time-series of reconstructed NAO and ENSO indices and GRIP stable isotopes

A propagation-separation approach to estimate the autocorrelation in a time-series

The paper presents an approach to estimate parameters of a local stationary AR(1) time series model by maximization of a local likelihood function. The method is based on a propagation-separation procedure that leads to data dependent weights defining the local model. Using free propagation of weights under homogeneity, the method is capable of separating the time series into intervals of approximate local stationarity. Parameters in different regions will be significantly different. Therefore the method also serves as a test for a stationary AR(1) model. The performance of the method is illustrated by applications to both synthetic data and real time-series of reconstructed NAO and ENSO indices and GRIP stable isotopes

Non-linear Hypothesis Testing of Geometric Object Properties of Shapes Applied to Hippocampi

This paper presents a novel method to test mean differences of geometric object properties (GOPs). The method is designed for data whose representations include both Euclidean and non-Euclidean elements. It is based on advanced statistical analysis methods such as backward means on spheres. We develop a suitable permutation test to find global and simultaneously individual morphological differences between two populations based on the GOPs. To demonstrate the sensitivity of the method, an analysis exploring differences between hippocampi of first-episode schizophrenics and controls is presented. Each hippocampus is represented by a discrete skeletal representation (s-rep). We investigate important model properties using the statistics of populations. These properties are highlighted by the s-rep model that allows accurate capture of the object interior and boundary while, by design, being suitable for statistical analysis of populations of objects. By supporting non-Euclidean GOPs such as direction vectors, the proposed hypothesis test is novel in the study of morphological shape differences. Suitable difference measures are proposed for each GOP. Both global and simultaneous GOP analyses showed statistically significant differences between the first-episode schizophrenics and controls

On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering

Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure plays a crucial role in the hierarchical merging. The dissimilarity measure has great impact on the final clustering, and data-independent properties are needed to choose the right dissimilarity measure for the problem at hand. Properties for distance-based dissimilarity measures have been studied for decades, but properties for density-based dissimilarity measures have so far received little attention. Here, we propose six data-independent properties to evaluate density-based dissimilarity measures associated with hybrid clustering, regarding equality, orthogonality, symmetry, outlier and noise observations, and light-tailed models for heavy-tailed clusters. The significance of the properties is investigated, and we study some well-known dissimilarity measures based on Shannon entropy, misclassification rate, Bhattacharyya distance and Kullback-Leibler divergence with respect to the proposed properties. As none of them satisfy all the proposed properties, we introduce a new dissimilarity measure based on the Kullback-Leibler information and show that it satisfies all proposed properties. The effect of the proposed properties is also illustrated on several real and simulated data sets