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ABSTRACTThe prevalence of diabetes is increasing dramatically, incurring a major health and socioeconomic burden. Type 2 diabetes (T2D), the most prevalent form of diabetes, results from a variable combination of insulin resistance and insulin deficiency, secondary to pancreatic β-cell failure. These defects are caused by a complex interplay between genetic and environmental/ lifestyle factors. Among the latter, poor dietary quality is a crucial driver of T2D development. Although adopting healthy dietary habits is considered as a mainstay for T2D prevention, what constitutes a healthy diet remains controversial. Epidemiological studies examining the association of dietary fat quality with T2D incidence have yielded equivocal results and may suffer from confounding. On the other hand, randomized trials assessing the impact of dietary fat saturation on glucose homeostasis have major methodological shortcomings, precluding reliable conclusions. In order to elucidate this question, we compared the effects of palm oil vs olive oil on glucose homeostasis and other relevant metabolic parameters, in a mouse model of high-fat diet-induced obesity. The saturated fatty acid-rich palm oil is the most abundantly used oil worldwide. Olive oil is a staple food of the Mediterranean diet, rich in monounsaturated fatty acids and widely regarded as healthful. In this model, palm oil was not more harmful than olive oil with regard to glucose/insulin homeostasis. However, palm oil was associated with increased visceral adiposity and triglyceridemia compared to olive oil. Circulating and tissue free fatty acid (FFA) concentration and composition are determined by dietary factors, as well as genetic and metabolic factors. There is accumulating evidence indicating that increased FFA levels and/or an unbalanced FFA composition with excess palmitate, induce β-cell dysfunction and apoptosis (lipotoxicity). To characterize the mechanisms underlying lipotoxicity, we combined RNA-sequencing with proteomics of β-cells exposed to palmitate, the most prevalent SFA in humans. This cross-omics study showed that palmitate altered lipid and amino-acid metabolism, and affected amplifying pathways of insulin secretion and exocytosis. Furthermore, palmitate induced stress pathways, including mitochondrial dysfunction, oxidative stress and endoplasmic reticulum (ER) stress. ER stress is triggered when protein folding demand exceeds ER folding capacity. This response aims to restore ER homeostasis but if unresolved, it can become deleterious. Islets from T2D patients display signs of ER stress, pointing to a potentially pathogenic role of the latter.Monogenic and neonatal diabetes are rare forms of diabetes caused by single gene mutations. These forms are of particular interest, as they can serve as ‘human knockout’ models of diabetes. Recent evidence shows that there is overlap in the genetic basis of monogenic diabetes and T2D, suggesting that they may be part of a pathologic continuum. To explore the role of ER stress in diabetes pathogenesis, we studied two different genetic syndromes involving neonatal or early-onset diabetes, caused by mutations in genes related to ER function (DNAJC3 and YIPF5). Using in vitro knockdown models, we showed that ER stress elicited by impaired chaperone function (DNAJC3) or by impaired ER-to-Golgi protein transport (YIFP5) causes β-cell apoptosis. Altogether, our findings support that lipotoxic and genetic ER stress contribute to diabetes pathogenesis. Preventing or modulating ER stress thus holds anti-diabetic therapeutic potential. Future research should focus on defining optimal strategies to restore a balanced FFA profile and enhance ER function, aiming to prevent ER-stress induced β-cell failure. RésuméLa prévalence du diabète progresse constamment, posant un défi sanitaire et socioéconomique majeur. Le diabète de type 2 (DT2), la forme la plus courante de diabète, résulte de la résistance à l’insuline, en association avec un déficit insulinique dû à la défaillance des cellules β pancréatiques. Ces anomalies découlent d’une interaction complexe entre des facteurs génétiques et des facteurs liés au mode de vie. Parmi ces derniers, la qualité du régime alimentaire est un facteur crucial pour le développement du DT2. Bien que le suivi d’un régime alimentaire sain est considéré comme le pilier pour la prévention du DT2, ce qui constitue un régime sain demeure un sujet de controverse.Les études épidémiologiques examinant l’association entre la qualité de la graisse alimentaire et l’incidence du DT2 ont donné des résultats équivoques, affectés éventuellement par des facteurs confondants. En outre, les études randomisées évaluant l’impact du degré de saturation de la graisse alimentaire sur l’homéostasie du glucose comportent des limitations méthodologiques majeures. Afin d’élucider cette question, on a comparé les effets de l’huile de palme aux effets de l’huile d’olive sur l’homéostasie du glucose et d’autres paramètres métaboliques pertinents. Dans ce but, on a utilisé un modèle murin d’obésité induite par un régime riche en graisse. L’huile de palme est riche en acides gras saturés et elle est l’huile la plus utilisée globalement. L’huile d’olive est un aliment phare du régime Méditerranéen, riche en acides gras monoinsaturés et généralement reconnu comme un aliment sain. Dans notre modèle murin, la consommation d’huile de palme n’était pas plus néfaste que celle de l’huile d’olive sur l’homéostasie du glucose, la sensibilité à l’insuline et l’insulinosécrétion. Par contre, l’huile de palme était associée à une adiposité viscérale et une triglycéridémie plus élevée comparée à l’huile d’olive.La concentration et la composition des acides gras libres (AGL) sont déterminées par des facteurs alimentaires, génétiques et métaboliques. Des données abondantes démontrent que la présence des niveaux élevés d’AGL et/ou d’une composition déséquilibrée d’AGL induit la dysfonction et l’apoptose des cellules β (lipotoxicité). Pour caractériser les mécanismes sous-jacents de la lipotoxicité, on a combiné un séquençage ARN à une étude protéomique des cellules β exposées au palmitate, l’AGL saturé le plus courant chez l’homme. Cette étude conjointe a montré que le palmitate altère le métabolisme des lipides et des acides aminés, les voies d’amplification de la sécrétion d’insuline et l’exocytose. Le palmitate induit également des voies de stress cellulaires, telles que la dysfonction mitochondriale, le stress oxydatif et le stress du réticulum endoplasmique (RE). Le stress du RE est activé quand les besoins en sécrétion protéique dépassent les capacités de l’organite. Cette réponse a pour but de rétablir l’homéostasie du RE mais si le stress reste non résolu, ceci peut s’avérer délétère. Des îlots des patients avec un DT2 montrent des signes de stress du RE, évoquant un rôle potentiellement pathogénique de ce dernier.Le diabète monogénique et néonatal sont des formes rares de diabète causées par des mutations d’un seul gène. Ces formes sont particulièrement intéressantes sur le plan physiopathologique car elles représentent des ‘knockout’ humains. Des données récentes montrent que la base génétique du diabète monogénique n’est pas complètement distincte de celle du diabète de type 2 et les deux entités pourraient faire partie d’un continuum. Afin d’explorer le rôle du stress du RE dans la pathogénèse du diabète, on a étudié deux syndromes génétiques entraînant un diabète néonatal ou à début très précoce. Ces syndromes sont causés par des mutations dans des gènes impliqués dans la fonction du RE (DNAJC3 et YIPF5). En silençant ces gènes in vitro, on a montré que le stress du RE, déclenché soit par une dysfonction des chaperones (DNAJC3), soit par un retard du trafic de protéines du RE vers le Golgi (YIPF5), induit l’apoptose des cellules β.Ces résultats suggèrent que le stress du RE génétique et lipotoxique contribuent à la pathogénèse du diabète. La prévention ou modulation du stress du RE présente donc un potentiel thérapeutique anti-diabétique. Des études futures pourraient permettre de définir des stratégies optimales pour rétablir un profil d’AGL équilibré ou renforcer la fonction du RE, en vue de prévenir la défaillance des cellules β. / Doctorat en Sciences médicales (Médecine) / info:eu-repo/semantics/nonPublished

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Polyploidization instantly doubles all genome content by combining two genomes that have markedly different methylation and gene expression levels. This process may be accompanied by genetic and epigenetic changes in each genome. Sequencing of the transcriptome (RNA-seq) and the methylome (bisulfite treated libraries whole genome libraries) were used to measure gene expression and methylation levels of genic regions of allopolyploid cotton petals and petals of their diploid relatives. Many differentially expressed genes detected by RNA-seq were consistent with expression levels previously detected by microarrays. RNA-seq results also reconfirmed the presence of general polyploid gene expression trends like expression level dominance and homoeologous expression biases in Gossypium polyploid species. Expression biases between A- and D-genome homoeologs and expression level dominance was characterized for thousands of genes in tetraploids and a diploid F1-hybrid. Unlike the results of microarray study previously done we found a slightly greater number of genes showing A-genome bias vs genes showing D-genome bias. More commonly the overall expression level from homoeologs of polyploid is heterotic i.e the expression level is greater than the average of the expression levels from the two parent genomes. In addition, genome methylation (CG, CHG, and CHH contexts) of each genome was assessed in the diploid and tetraploid samples. The A- and D-genomes had distinct levels of DNA methylation for each context. DNA methylation may be independently regulating homoeologous expression levels of a small number of genes.

Globally, cancer is one of the leading causes of mortality, with an estimated 18.1 million cancer cases, 10 million deaths, and 1.9 million new cases diagnosed in 2020 (Sung et al., 2021). However, during the past several decades, cancer survival has improved such that 82% of children and >2/3 of adults diagnosed with cancer will survive beyond five years (World Health Organization (WHO) - Childhood Cancer, 2021). Skeletal muscle atrophy and fibrosis are long-term adverse effects experienced by 80% of cancer survivors for which there is no available therapy (Paulino, 2004). These long-term consequences are related to the toxicity from the cancer treatment, leading to alterations in skeletal muscle function which can lead to comorbidities and increased mortality among cancer survivors (Paulino, 2004; Williams et al., 2016). Thus, novel approaches to address the long-term effects of cancer therapy on skeletal muscle are critically needed. Exercise training is a potential non-pharmacological strategy that improves common cancer- and treatment-related side effects (Mustian et al., 2012). Specifically, exercise programs that combine resistance and endurance training (RET) have been shown to improve muscle strength and cardiovascular fitness in cancer survivors (Tong et al., 2020). The mechanisms responsible for these effects remain unknown. The remarkable plasticity of skeletal muscle relies primarily on muscle stem (satellite) cells (MuSCs) (Lepper et al., 2011) that are regulated, in part, by muscle-resident stromal cells (Bentzinger et al., 2013). These different stromal cell types, including: vascular endothelial cells (ECs), immune cells, and mesenchymal progenitors, also known as fibro-adipogenic progenitors (FAPs), create the muscle stem cell niche (Yin et al., 2013). FAPs possess a dual role as they are involved in skeletal muscle maintenance and regeneration by secreting pro-myogenic trophic factors (Biferali et al., 2019; Joe et al., 2010; Uezumi et al., 2010; Wosczyna et al., 2019), but also contribute to fibrotic and fatty tissue accumulation in chronic degenerative conditions (Uezumi et al., 2010). The divergent features of FAPs highly depend on signals they receive from their microenvironment (Giuliani et al., 2021); however, FAP's contribution to cancer treatment-induced muscle pathology in cancer survivors remains unknown. The overall objective of this thesis is to begin to develop an understanding of the role of FAPs in cancer treatment-induced muscle pathology and to determine if RET represents an effective therapy to prevent the long-term muscle defects of juvenile cancer plus therapy.

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

Cell differentiation is the process of a cell developing from one cell type to another. It is of interest to analyse the differentiation from stem cells to different types of mature cells, and discover what genes are involved in regulating the differentiation to specific cells, for instance to get insights to what is causing certain diseases and find potential treatments.  In this project, two mathematical models are developed for analysing blood cell differentiation (haematopoiesis) with methods based on optimal transportation. Optimal transportation is about moving one mass distribution to another at minimal cost. Modelling a sample of cells as point masses placed in a space based on the cells' gene expressions, accessed by single-cell RNA sequencing, optimal transportation is used to find transitions between cells that costs the least in terms of changes in gene expression. With this, cell-to-cell trajectories, from haematopoietic stem cells to mature blood cells, are obtained. With the first model, cells are divided into groups based on their maturity, which is determined by using diffusion pseudotime, and optimal transportation is preformed between groups. The resulting trajectories suggest that haematopoietic stem cells possibly can develop into the same mature cell type in different ways, and that the cell fate for some cell types is decided late on in development. In future work, the gene regulation along the obtained trajectories can be analysed. The second model is developed to be more general than the first, and not be dependent on a group division before preforming optimal transportation. / Celldifferentiering är processen då en cell utvecklas från en celltyp till en annan. Det är av intresse att analysera differentieringen från stamcell till olika typer av mogna celler, och undersöka vilka gener som har betydelse i regleringen av differentieringen till specifika celler, bland annat för att få en inblick i vad som orsakar vissa sjukdomar och hitta potentiella botemedel. I detta projekt utvecklas två matematiska modeller för att analysera blodcellsutveckling (hematopoes) med metoder som är baserade på optimal transport. Optimal transport handlar om att förflytta en massfördelning till en annan till lägst kostnad. Genom att modellera celler som punktmassor, placerade i ett rum baserat på cellernas genuttryck som fås genom singel-cell RNA-sekvensering, används optimal transport för att hitta förflyttningar mellan celler som kostar minst i termer av förändringar i genuttryck. Från detta skapas vägar mellan celler, från hematopoetiska stamceller till mogna celler. I den första modellen delas cellerna upp i grupper baserat på deras mognadsgrad, som bestäms genom att använda pseudotid baserad på en diffusionsavbildning, och optimal transport används sedan mellan grupperna. De resulterande vägarna visar på att hematopoetiska stamceller möjligen kan utvecklas till samma typ av mogen cell på olika sätt, och att cellödet för vissa typer av celler bestäms sent i utvecklingen. I framtida arbete kan genregleringen längs de funna vägarna analyseras. Den andra modellen utvecklas för att vara mer generell än den första, och inte bero på en gruppuppdelning innan optimal transport används.