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Human white adipose tissue is a metabolically active organ with distinct depot-specific functions. Despite their locations close to the gastrointestinal tract, mesenteric adipose tissue and epiploic adipose tissue have only scarcely been investigated. The aim is to characterise these adipose tissues in-depth and estimate their contribution to alterations in whole-body metabolism. While mesenteric adipose tissue exhibited signatures similar to those found in the omental depot, epiploic adipose tissue was distinct from all other studied fat depots. Multiomics allowed clear discrimination between the insulin sensitive and insulin resistance states in all tissues. The highest discriminatory power between insulin sensitivity and insulin resistance was seen in epiploic adipose tissue, where profound differences in the regulation of developmental, metabolic and inflammatory pathways were observed. Gene expression levels of key molecules involved in adipose tissue function, metabolic homeostasis and inflammation revealed significant depot-specific differences with epiploic adipose tissue showing the highest expression levels. Multi-omics epiploic adipose tissue signatures reflect systemic insulin resistance and obesity subphenotypes distinct from other fat depots. These data suggest a previously unrecognised role of human epiploic fat in the context of obesity, impaired insulin sensitivity and related diseases.:Introduction...................................................................................................................3 Publication..................................................................................................................11 Summary.....................................................................................................................25 Bibliography................................................................................................................28 Supplements...............................................................................................................30

With increasing data and technology, multiple types of data from the same set of nodes have been generated. Since each data modality contains a unique aspect of the underlying mechanisms, multiple datatypes are integrated. In addition to multiple datatypes, networks are important to store information representing associations between entities such as genes of a protein-protein interaction network and authors of a citation network. Recently, some advanced approaches to graph-structured data leverage node associations and features simultaneously, called Graph Neural Network (GNN), but they have limitations for integrative approaches. The overall aim of this dissertation is to integrate multiple data modalities on graph-structured data to infer some context-specific gene regulation and predict outcomes of interest. To this end, first, we introduce a computational tool named CRINET to infer genome-wide competing endogenous RNA (ceRNA) networks. By integrating multiple data properly, we had a better understanding of gene regulatory circuitry addressing important drawbacks pertaining to ceRNA regulation. We tested CRINET on breast cancer data and found that ceRNA interactions and groups were significantly enriched in the cancer-related genes and processes. CRINET-inferred ceRNA groups supported the studies claiming the relation between immunotherapy and cancer. Second, we present SUPREME, a node classification framework, by comprehensively analyzing multiple data and associations between nodes with graph convolutions on multiple networks. Our results on survival analysis suggested that SUPREME could demystify the characteristics of classes with proper utilization of multiple data and networks. Finally, we introduce an attention-aware fusion approach, called GRAF, which fuses multiple networks and utilizes attention mechanisms on graph-structured data. Utilization of learned node- and association-level attention with network fusion allowed us to prioritize the edges properly, leading to improvement in the prediction results. Given the findings of all three tools and their outperformance over state-of-the-art methods, the proposed dissertation shows the importance of integrating multiple types of data and the exploitation of multiple graph structured data.

Objective Human white adipose tissue (AT) is a metabolically active organ with distinct depot- specific functions. Despite their locations close to the gastrointestinal tract, mesenteric AT and epiploic AT (epiAT) have only scarcely been investigated. Here, we aim to characterise these ATs in-depth and estimate their contribution to alterations in whole-body metabolism. Design Mesenteric, epiploic, omental and abdominal subcutaneous ATs were collected from 70 patients with obesity undergoing Roux-en-Y gastric bypass surgery. The metabolically well-characterised cohort included nine subjects with insulin sensitive (IS) obesity, whose AT samples were analysed in a multiomics approach, including methylome, transcriptome and proteome along with samples from subjects with insulin resistance (IR) matched for age, sex and body mass index (n=9). Findings implying differences between AT depots in these subgroups were validated in the entire cohort (n=70) by quantitative real-time PCR. Results While mesenteric AT exhibited signatures similar to those found in the omental depot, epiAT was distinct from all other studied fat depots. Multiomics allowed clear discrimination between the IS and IR states in all tissues. The highest discriminatory power between IS and IR was seen in epiAT, where profound differences in the regulation of developmental, metabolic and inflammatory pathways were observed. Gene expression levels of key molecules involved in AT function, metabolic homeostasis and inflammation revealed significant depot- specific differences with epiAT showing the highest expression levels. Conclusion Multi- omics epiAT signatures reflect systemic IR and obesity subphenotypes distinct from other fat depots. Our data suggest a previously unrecognised role of human epiploic fat in the context of obesity, impaired insulin sensitivity and related diseases.

Yes / Widespread generation and analysis of omics data have revolutionized molecular medicine on Earth, yet its power to yield new mechanistic insights and improve occupational health during spaceflight is still to be fully realized in humans. Nevertheless, rapid technological advancements and ever-regular spaceflight programs mean that longitudinal, standardized, and cost-effective collection of human space omics data are firmly within reach. Here, we consider the practicality and scientific return of different sampling methods and omic types in the context of human spaceflight. We also appraise ethical and legal considerations pertinent to omics data derived from European astronauts and spaceflight participants (SFPs). Ultimately, we propose that a routine omics collection program in spaceflight and analog environments presents a golden opportunity. Unlocking this bright future of artificial intelligence (AI)-driven analyses and personalized medicine approaches will require further investigation into best practices, including policy design and standardization of omics data, metadata, and sampling methods. / H.C., R.H., J.B., D.B., S.G., T.E., and N.J.S. are members of the ESA Space Omics Topical Team, funded by the ESA grant/contract 4000131202/20/NL/PG/pt “Space Omics: Towards an integrated ESA/NASA –omics database for spaceflight and ground facilities experiments” awarded to R.H., which was the main funding source for this work. H.C. is also supported by the Horizon Center for Doctoral Training at the University of Nottingham (UKRI grant no. EP/S023305/1). S.G. is supported by the Swedish Research Council VR grant 2020-04864. L.A.R. and M.M. represent the Omics Subgroup of the Japan Society for the Promotion of Science KAKENHI funding group “Living in Space” and are supported by JP15K21745, JP20H03234, and 20F20382. L.A.R. is also supported by the JSPS postdoctoral fellowship P20382. We thank Dr. Sarah Castro-Wallace, the NASA GeneLab Animal AWG, ISSOP, ESA Space Omics Topical Team, ESA Personalized Medicine Topical Team, and Global Alliance for Genomic Health (GA4GH) for useful discussions.

La fibrillation auriculaire (FA) est l'arythmie cardiaque la plus répandue dans le monde et est associée à une hausse de morbidité et une mortalité importante. Des progrès substantiels dans notre compréhension de l'étiologie de la maladie ont été réalisés au cours des deux dernières décennies, conduisant à une amélioration du traitement et de la gestion de la maladie. Cependant, le fardeau de la FA continue d'augmenter. De plus, les mécanismes moléculaires et cellulaires sous-jacents à l’initiation et à la progression de la FA restent incomplètement compris. Dans cette thèse, mon objectif était de caractériser de nouveaux déterminants moléculaires et cellulaires de la FA en utilisant une approche multiomique. J'ai d'abord utilisé le séquençage de l'ARN (RNAseq) pour l'ARN total et les micro-ARN (miRNA) afin de dévaluer l'effet de la FA sur l’expression génique dans deux modèles canins de FA. Ces résultats ont impliqué le locus orthologue humain 14q32 et son lien potentiel avec la signalisation du glutamate. Dans le chapitre trois, j'ai démontré les lacunes actuelles des modèles statistiques utilisés pour prédire l'effet régulateur des régions de chromatine ouvertes sur l'expression des gènes dans des essais multiomiques à noyau unique et suggéré des alternatives montrant un meilleur pouvoir prédictif. Dans le chapitre quatre, j'ai utilisé des analyses par locus quantitatifs d'expression (eQTL) pour caractériser les variants génétiques communs associés à la FA. Grâce à des analyses de colocalisation, une cartographie fine et un multiome à noyau unique, j'ai justifié mécaniquement l’effet de variants non-codants et fait la priorisation de gènes candidats, notamment GNB4, MAPT et LINC01629. Enfin, dans le chapitre cinq, j'ai fourni une caractérisation approfondie des gènes persistants de FA différentiellement exprimés au niveau cellulaire et identifié les facteurs de transcription potentiels impliqués dans leur régulation. En résumé, l’utilisation d’une approche multiomique a permis de découvrir de nombreuses nouvelles voies cellulaires et génétiques modifiées au cours de la FA ainsi que des gènes candidats impliqués dans le risque génétique de la FA. Ces résultats fournissent des informations et ressources importantes pour concevoir de nouvelles stratégies thérapeutiques, impliquant à la fois des cibles génétiques et nouvelles voies cellulaires pour lutter contre cette maladie cardiaque commune. / Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is associated with important morbidity and mortality. Substantial advancement in our understanding of the disease etiology have been made in the past two decades leading to improved treatment and management of the disease, however, AF burden continues to increase. Moreover, the molecular and cellular mechanisms underlying AF initiation and progression remain incompletely understood. In this thesis I aimed to characterise novel molecular and cellular determinants of AF using a multiomic approache. In chapter two, I used RNA sequencing (RNAseq) for total RNA and micro-RNAs (miRNA) to decipher the effect of AF in two canine models, which implicated the orthologue human locus 14q32 and its potential role in glutamate signaling regulation. In chapter three, I demonstrated current shortcomings of statistical models used to predict the regulatory effect of open chromatin regions on gene expression in single nuclei multiomic assays and suggested alternatives showing better predictive power. In chapter four, I used expression quantitative loci (eQTL) to characterize AF associated common genetic variants. Through co-localization analyses, fine-mapping and single nuclei multiome, I mechanistically substantiated non-coding variants and prioritized strong candidate genes including GNB4, MAPT and LINC01629. Finally, in chapter five, I provided a deep characterization of persistent AF differentially expressed genes (DEGs) at the cellular level and identified potential transcription factors involved in their regulation. In summary, using a multiomic approach unraveled numerous new cellular and gene pathways altered during AF and candidate genes implicated in AF genetic risk. These findings provide important insights and data resources to design novel therapeutic strategies, targeting both genetically derived candidate genes and cellular pathways to address this pervasive cardiac disease.

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