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

Mitochondrial dynamics and quantification of mitochondria-derived vesicles in cardiomyoblasts using structured illumination microscopy

Mitochondria are essential energy-providing organelles of particular importance in energy-demanding tissue such as the heart. The production of mitochondria-derived vesicles (MDVs) is a cellular mechanism by which cells ensure a healthy pool of mitochondria. These vesicles are small and fast-moving objects not easily captured by imaging. In this work, we have tested the ability of the optical super-resolution technique 3DSIM to capture high-resolution images of MDVs. We optimized the imaging conditions both for high-speed video microscopy and fixed-cell imaging and analysis. From the 3DSIM videos, we observed an abundance of MDVs and many dynamic mitochondrial tubules. The density of MDVs in cells was compared for cells under normal growth conditions and cells during metabolic perturbation. Our results indicate a higher abundance of MDVs in H9c2 cells during glucose deprivation compared with cells under normal growth conditions. Furthermore, the results reveal a large untapped potential of 3DSIM in MDV research

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care

High-resolution visualization and assessment of basal and OXPHOS-induced mitophagy in H9c2 cardiomyoblasts

Mitochondria are susceptible to damage resulting from their activity as energy providers. Damaged mitochondria can cause harm to the cell and thus mitochondria are subjected to elaborate quality-control mechanisms including elimination via lysosomal degradation in a process termed mitophagy. Basal mitophagy is a house-keeping mechanism fine-tuning the number of mitochondria according to the metabolic state of the cell. However, the molecular mechanisms underlying basal mitophagy remain largely elusive. In this study, we visualized and assessed the level of mitophagy in H9c2 cardiomyoblasts at basal conditions and after OXPHOS induction by galactose adaptation. We used cells with a stable expression of a pH-sensitive fluorescent mitochondrial reporter and applied state-of-the-art imaging techniques and image analysis. Our data showed a significant increase in acidic mitochondria after galactose adaptation. Using a machine-learning approach we also demonstrated increased mitochondrial fragmentation by OXPHOS induction. Furthermore, super-resolution microscopy of live cells enabled capturing of mitochondrial fragments within lysosomes as well as dynamic transfer of mitochondrial contents to lysosomes. Applying correlative light and electron microscopy we revealed the ultrastructure of the acidic mitochondria confirming their proximity to the mitochondrial network, ER and lysosomes. Finally, exploiting siRNA knockdown strategy combined with flux perturbation with lysosomal inhibitors, we demonstrated the importance of both canonical as well as non-canonical autophagy mediators in lysosomal degradation of mitochondria after OXPHOS induction. Taken together, our high-resolution imaging approaches applied on H9c2 cells provide novel insights on mitophagy during physiologically relevant conditions. The implication of redundant underlying mechanisms highlights the fundamental importance of mitophagy

Computational study of SERT using new X-ray crystal structures and initial experimental verification of potential GABAB receptor compounds

Depression is currently one of the leading causes worldwide of suicide and disability, the most common treatment is antidepressants. Most antidepressants work by increasing the monoamine levels in the central nervous system by inhibition of the reuptake of monoamines into the presynaptic neuron, and thereby ensure accumulation of the monoamines in the synaptic cleft. The main target for most antidepressants is the serotonin transporter, which is responsible for transporting serotonin, a monoamine, from the synaptic cleft back into the presynaptic neuron. The antidepressant (S)-citalopram is an antidepressant targeting the serotonin transporter that is well tolerated among patient populations. It is also the inhibitor that was rendered with best resolution in the recent crystal structures of the serotonin transporter. A computational study of the serotonin transporter with a focus on key amino acids for its function in both the central and allosteric sites using (S)-citalopram and its substrate serotonin as ligands in four MD simulations was performed. This shed light on some of the internal molecular mechanisms of the serotonin transporter, especially the interactions at the binding sites. Each simulation identified key amino acids between each respective ligand and binding site. For the central site simulation there was identified one key amino acid, TYR95, that both ligands had as their primary interaction and point of contact. There was also seen a disparity between (S)-citalopram and serotonin in terms of interaction types, with their preferences being hydrophobic and H-bonds respectively. For the allosteric site simulations, the interaction type trends were the same as for the central site. In the allosteric simulations the amino acids PHE335 and GLU494 were identified as the strongest interacting partners for (S)-citalopram and serotonin respectively. The interactions between the ligands and the allosteric site were also not as strong overall as the interactions of the central site. A docking study was also performed with verified inhibitors of the serotonin transporter that sought to investigate binding site interactions between the docked inhibitors and the protein. An overview of amino acid interactions was created, which allowed for the identification of amino acids that interacted with the vast majority of ligands docked into the respective binding sites. The data from the docking was also used to attempt to elucidate a connection between in silico and experimental results. In order for the study to involve experimental work an initial functional cAMP assay screening was incorporated into the project from an ongoing study in the research group. The assay tested 10 compounds that had been identified through a virtual screening approach as being potential GABAB receptor antagonists. None of the 10 compounds showed antagonistic effects on the GABAB receptor, but there were indications that three of them may be agonists. Further assays are required to confirm the three compounds status as agonists. The experimental work provided experience working in a laboratory environment and carrying out every step of a functional cAMP assay

Causality in Scale Space as an Approach to Change Detection

Kernel density estimation and kernel regression are useful ways to visualize and assess the structure of data. Using these techniques we define a temporal scale space as the vector space spanned by bandwidth and a temporal variable. In this space significance regions that reflect a significant derivative in the kernel smooth similar to those of SiZer (Significant Zero-crossings of derivatives) are indicated. Significance regions are established by hypothesis tests for significant gradient at every point in scale space. Causality is imposed onto the space by restricting to kernels with left-bounded or finite support and shifting kernels forward. We show that these adjustments to the methodology enable early detection of changes in time series constituting live surveillance systems of either count data or unevenly sampled measurements. Warning delays are comparable to standard techniques though comparison shows that other techniques may be better suited for single-scale problems. Our method reliably detects change points even with little to no knowledge about the relevant scale of the problem. Hence the technique will be applicable for a large variety of sources without tailoring. Furthermore this technique enables us to obtain a retrospective reliable interval estimate of the time of a change point rather than a point estimate. We apply the technique to disease outbreak detection based on laboratory confirmed cases for pertussis and influenza as well as blood glucose concentration obtained from patients with diabetes type 1

Instance Segmentation of Microscopic Foraminifera

Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of 0.78 on the classification and detection task, and 0.80 on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to 0.84 and 0.86, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifer

True positive rate for detection algorithms.

<p>Number of times each algorithm eventually detected a change for the combinations of magnitude of change Δ and binwidth <i>b</i>. Note that for cSiZer all changes were found, giving a true positive rate of 1.</p

Schematic definition of the causal scale space.

<p>An event at time has a causal region in the light gray area and an effective causal region in the dark gray area between the dashed lines. If we detect significance in the interval shown by the vertical thick bar, the past causal region common to all points in this region of scale space infers an originating event in the region indicated by the horizontal bar below the <i>t</i>-axis, which encompasses the true event. The definitions of and are illustrated by drawing the kernel at one scale . The definition of the effective causal region and hence the s follow accordingly as described in the text.</p

The fitted values of and for various <i>p</i>.

<p>The inset shows the quality of the fit measured by the root mean square error over the same range of . is highlighted by a dashed vertical line.</p