Indiana University-Purdue University Indianapolis (IUPUI) / The successful treatment and potential eradication of many complex diseases, such as cancer, begins with elucidating the convoluted mapping of molecular profiles to phenotypical manifestation. Our observed molecular profiles (e.g., genomics, transcriptomics, epigenomics) are often high-dimensional and are collected from patient samples falling into heterogeneous disease subtypes. Interpretable learning from such data calls for sparsity-driven models. This dissertation addresses the high dimensionality, sparsity, and heterogeneity issues when analyzing multiple-omics data, where each method is implemented with a concomitant R package. First, we examine challenges in submatrix identification, which aims to find subgroups of samples that behave similarly across a subset of features. We resolve issues such as two-way sparsity, non-orthogonality, and parameter tuning with an adaptive thresholding procedure on the singular vectors computed via orthogonal iteration. We validate the method with simulation analysis and apply it to an Alzheimer’s disease dataset. The second project focuses on modeling relationships between large, matched datasets. Exploring regressional structures between large data sets can provide insights such as the effect of long-range epigenetic influences on gene expression. We present a high-dimensional version of mixture multivariate regression to detect patient clusters, each with different correlation structures of matched-omics datasets. Results are validated via simulation and applied to matched-omics data sets. In the third project, we introduce a novel approach to modeling spatial transcriptomics (ST) data with a spatially penalized multinomial model of the expression counts. This method solves the low-rank structures of zero-inflated ST data with spatial smoothness constraints. We validate the model using manual cell structure annotations of human brain samples. We then applied this technique to additional ST datasets. / 2025-05-22

Cancer is a dangerous disease caused by mutations in the host's genome that makes the cells proliferateuncontrollably and disrupts bodily functions. The immune system tries to prevent this, but tumours have methods ofdisrupting the immune system's ability to combat the cancer. These immunosuppression events can for examplehappen when the immune system interacts with the tumour to recognise it or try and destroy it. The tumours can bychanging their displayed proteins on the cell surface avoid detection or by excreting proteins, they can neutralisedangerous immune cells. This happens within the tumour microenvironment (TME), the immediate surrounding of atumour where there is a plethora of different cells both aiding and suppressing the tumour. Some of these cells arenot cancer cells but can still aid the tumour due to how the tumour has influenced them. For example, throughangiogenesis, where new blood vessels are formed which feeds the tumour. The interactions in the TME can be used as a target for immunotherapy, a field of treatments which improves theimmune system's own ability at defending against cancer. Immunotherapy can for example help the immune systemby guiding immune cells towards the tumour. It is therefore essential to understand the complex system ofinteractions within the TME to be able to create new methods of immunotherapy and thus treat cancers moreefficiently. Concurrently new methods of mapping what happens in a tissue have been developed in recent years,namely spatial transcriptomics (ST). It allows for the retrieval of transcriptomic information of cells throughsequencing while still retaining spatial information. However, the ST methods which capture the wholetranscriptome of the cells and reveal the cell-to-cell interactions are not of single-cell resolution yet. They capturemultiple cells in each spot, creating a mix of cells in the sequencing. This mix of cells can be detangled, and theproportions of each cell type revealed through the process of deconvolution. Deconvolution works by mapping thesingle cell expression profile of different cell types onto the ST data and figuring out what proportions of expressioneach cell type produces the expression of the mix. This reveals the cellular composition of the microenvironment.But since the interactions in the TME depend on the cells current expression we need to deconvolute according tophenotype and not just cell type. In this project we were able to create a tool which automatically finds phenotypes in the single-cell data and usesthose phenotypes to deconvolute ST data. Phenotypes are found using dimensionality reduction methods todifferentiate cells according to their contribution to the variability in the data. The resulting deconvoluted data wasthen used as the foundation for describing the growth of a cancer as a system of phenotype proportions in the tumourmicroenvironment. From this system a mathematical model was created which predicts the growth and couldprovide insight into how the phenotypes interact. The tool created worked as intended and the model explains thegrowth of a tumour in the TME with not just cancer cells phenotypes but other cell phenotypes as well. However, nonew interaction could be discovered by the final model and no phenotype found could provide us with new insightsto the structure of the TME. But our analysis was able to identify structures we expect to see in a tumour, eventhough they might not be so obvious, so an improved version of our tools might be able to find even more detailsand perhaps new, more subtle interactions.

Data visualization is an essential methodology for bioinformatics studies. Spatial Transcriptomics(ST) is a method that aims at measuring the transcriptome of tissue sections while maintaining its spacial information. Finally, the study of biological pathway focuses on a series of biochemical reactions that take place in organisms. As these studies generate a large number of datasets, this thesis attempts to combine the ST’s data with pathwayinformation and visualize it in an intuitive way to assist user comprehension and insight.In this thesis, Python was used for integrating the dataset and JavaScript libraries wereused for building the visualization. The processing of ST pathway data together with the data visualization interface are the outcomes of this thesis. The data visualization can show the regulation of pathways in the ST data and can be accessed by modern browsers. These outcomes can help users navigate the ST and pathway datasets more effectively. / Datavisualisering är en viktig del av bioinformatik. Spatial transkriptomik (ST) är en metod som mäter transkriptom, samtidigt som den behåller spatial information. Biologiskapathways å andrasidan fokuserar på biokemiska reaktioner som sker inom organismer. Dessa studier genererar mycket data, och denna avhandling försöker att kombinera ST-data med pathway information och få en intuitiv visualisering av det integrerade datat.I avhandlingen användes Python för att integrera datat och JavaScript bibliotek för attbygga visualiseringsverktyget. Avhandlingen resulterade i en metod för att integrera STdata och pathway information, samt ett visualiseringsverktyg för ovan nämnda information.Verktyget kan visa pathway regulationer i ST data och kan användas i moderna webbläsare.Forskningen resulterade i ett verktyg som kan hjälpa forskare att förstå ST och pathwaydata.

Ökningen av sekvenseringsdata har skapat ett behov av att ta fram nya och flexibla analysmetoder för att kunna analysera datan. Många sekvenseringsteknologier har utvecklats genom åren, med olika syften och de är idag mer specialiserade. Kostnaden för att sekvensera har även sjunkit kraftigt och idag är kostnaden bara en bråkdel av kostnaden för 20 år sedan.   En av dessa heter Spatial Transcriptomics där mRNA kan analyseras med Spatiell upplösning. Experimenten skapar stora mängder data och analysmetoder som ursprungligen var utvecklade för scRNA-seq har nu ocksp blivit mer specialiserade mot spatial data. En analysmetod som använts länge är Seurat som utvecklades av Satija labbet under 2015. Men de senaste åren har även nya metoder utvecklats. Två av dessa, Giotto och Squidpy kommer att jämföras med Seurat som referens för att reda ut hur bra de presterar för Spatial Transcriptomics analyser. Datan som kommer användas kommer från hjärnvävnad från fyra olika möss som testades i NASAs RR3 mission. Två av mössen är av ”flight” skick och kommer jämföras med två stycken ”ground” kontroller. I data analysen kommer Quality Control, Normalization, Integration, Dimensional reduction, Clustering och Differential Expression analysis testas. Förutom de steg som testas i analysen kommer även parametrar som analysmetodernas flexibilitet, duration och prestation att testas och jämföras. Resultaten i detta projekt visade att Seurat presterar bättre än Giotto och Squidpy utifrån de parametrar som testas. / The increase in data received from sequencing has created a need for new and accurate frameworks to analyze the data. There are many sequencing technologies developed for different purposes. They have become more specialized and the cost compared to 20 years ago is just a fraction. One of the technologies is Spatial Transcriptomics, where mRNA can be analyzed with spatial resolution. The experiments has high throughput, and frameworks that was original developed for scRNA-seq has also started to be more specialised towards spatial data. Seurat has been widely used for that purpose for many years and was developed by the Satija Lab. But many more frameworks have been developed. In this project’s scope, two other frameworks, Giotto and Squidpy, will be benchmarked with Seurat as the golden standard and a referece to examine how the frameworks perform with Spatial Transcriptomics data as input. The dataset consists of four mouse brain tissue sections from the NASA RR3 mission. Two of the mouse brains are of ”flight” condition while the two others are used as ”ground” controls. The pipeline used in all three frameworks includes Quality Control, normalization, integration, dimensional reduction, clustering, and differential expression analysis. Except for the pipeline steps other parameters has been tested including: the flexibility of the frameworks, the duration of analysis, and the performance. The results showed that Seurat outperforms Giotto and Squidpy according to the tested parameters. Mainly because of more developed integration features when working with multiple data. But both Squidpy and Giotto shows great potential and has a lot of features that was not useful for this project, but however can for other projects be very promising.

Indiana University-Purdue University Indianapolis (IUPUI) / Background: Cancer is the leading cause of death worldwide and as a result is one of the most studied topics in public health. Breast cancer and prostate cancer are the most common cancers among women and men respectively. Gene expression and image features are independently prognostic of patient survival. However, it is sometimes difficult to discern how the molecular profile, e.g., gene expression, of given cells relate to their spatial layout, i.e., topology, in the tumor microenvironment (TME). However, with the advent of spatial transcriptomics (ST) and integrative bioinformatics analysis techniques, we are now able to better understand the TME of common cancers. Method: In this paper, we aim to determine the genes that are correlated with image topology features (ITFs) in common cancers which we denote topology associated genes (TAGs). To achieve this objective, we generate the correlation coefficient between genes and image features after identifying the optimal number of clusters for each of them. Applying this correlation matrix to heatmap using R package pheatmap to visualize the correlation between the two sets. The objective of this study is to identify common themes for the genes correlated with ITFs and we can pursue this using functional enrichment analysis. Moreover, we also find the similarity between gene clusters and some image features clusters using the ranking of correlation coefficient in order to identify, compare and contrast the TAGs across breast and prostate cancer ST slides. Result: The analysis shows that there are groups of gene ontology terms that are common within breast cancer, prostate cancer, and across both cancers. Notably, extracellular matrix (ECM) related terms appeared regularly in all ST slides. Conclusion: We identified TAGs in every ST slide regardless of cancer type. These TAGs were enriched for ontology terms that add context to the ITFs generated from ST cancer slides.

Räumliche Transkriptomik-Sequenzierungstechniken werden bei der Untersuchung von RNA in komplexen Geweben immer populärer. Mit diesen neuartigen Ansätze wird die Häufigkeit von Transkripten unter Beibehaltung ihrer räumlichen Lage gemessen, und ermöglichen so die Untersuchung der Genexpression in einem unvoreingenommen, raumzeitlichen Kontext. Angesichts der Vielfalt der zugrunde liegenden experimentellen Techniken, die Datensätze, die von verschiedenen Transkriptomik-Assays erstellt werden, variieren stark. Diese Datensätze werden von Pipelines verarbeitet und analysiert, die speziell für die jeweilige Methode entwickelt sind. Sie sind weder einfach modifizierbar, noch erweiterbar, dadurch sind sie nicht mit Inputs anderer Technologien kompatibel. Hier wird spacemake vorgestellt, eine bioinformatische Software, die darauf abzielt, die Lücke zwischen den verschiedenen räumlichen transkriptomischen Sequenzierungsansätzen zu schließen, durch sie einheitliches, schnelles, modulares, reproduzierbares und erweiterbares Rahmenwerk für die Verarbeitung und Analyse groß angelegter räumlicher transkriptomischer Daten bietet. Spacemake verarbeitet erfolgreich Daten aus den neuesten räumlichen Transkriptomik-Assays, unabhängig von ihrer Inputs. Spacemake ist parallel und läuft im Vergleich zu anderen vergleichbaren Techniken schneller. Spacemake ist modular entwickelt, und bietet verschiedene Module wie automatisiertes Clustering und Analyse, Quality Control, Saturation Analyse durch Downsampling, Zusammenführung technischer Replikate, Integration von scRNA-seq-Daten und Alignment von Mikroskopiebildern. Um ein Höchstmaß an Flexibilität zu bieten, ermöglicht spacemake benutzerkonfigurierbare Einstellungen\textit{run-mode} Einstellungen, wodurch die Unterstützung einer breiten Palette experimenteller Designs gewährleistet wird. Da spacemake in Python geschrieben ist, lässt es sich gut mit anderen Computational Biologie Methoden integrieren. Insgesamt hat spacemake das Potenzial, ein wichtiger Bestandteil der räumlichen Transkriptomik-Toolbox der Gegenwart und Zukunft zu sein. / Spatial transcriptomics sequencing techniques are increasingly popular when studying RNA in complex tissues. These novel approaches measure the abundance of transcripts while retaining their spatial location information, thus allowing the study of gene expression in an unbiased, spatiotemporal context. Given the variety of the underlying experimental techniques, the datasets which are produced by each spatial transcriptomic assay also vary greatly. These datasets are processed and analyzed by pipelines tailored specifically for each method, and are not easily modifiable nor extendable, thus making them incompatible to work with inputs from other technologies. Here spacemake is introduced, a bioinformatic software that aims to close the gap between the various spatial transcriptomic sequencing approaches, by providing a unified, fast, modular, reproducible, and extendable framework for large-scale spatial transcriptomic data processing and analysis. Spacemake successfully processes data from the latest spatial transcriptomics assays, regardless of their input data structure. Spacemake is parallel and runs faster when compared with other similar methods. It has a modular design and offers several modules such as automated clustering and analysis, quality control, saturation analysis through downsampling, technical replicate merging, scRNA-seq data integration, and microscopy image alignment. To offer maximum flexibility, spacemake allows for user-configurable \textit{run-mode} settings, ensuring support for a wide range of experimental designs. Written in Python, spacemake integrates well with other computational biology solutions. Overall spacemake has the potential to be an important part of the spatial transcriptomics toolbox of the present and future.

Zirkuläre RNAs sind eine Klasse endogener, tierischer RNAs. Obwohl sie hoch abundant sind, ist weder ihre Funktion noch ihre Expression im Nervensystem bekannt. Es wurde ein Katalog zirkulärer RNAs in neuralen Proben erstellt. Es konnten tausende zirkuläre RNAs von Mensch und Maus entdeckt und analysiert werden. Zirkuläre RNAs sind außerordentlich angereichert im Säugetiergehirn, ihre Sequenz ist gut konserviert und sie sind häufig gemeinsam in Mensch und Maus exprimiert. Zirkuläre RNAs waren generell höher exprimiert im Verlauf der neuronalen Differenzierung, sind stark angereichert an Synapsen und oft differentiell exprimiert. circSLC45A4 ist die Hauptisoform, die in humanem präfrontalem, embryonalen Cortex von diesem genomischen Lokus exprimiert wird und eine der am höchsten exprimierten zirkulären RNAs in diesem System. Induzierte Verminderung der Expression von circSLC45A4 ist ausreichend, um die spontane neuronale Differenzierung einer humanen Neuroblastomzelllinie zu induzieren. Dies kann durch die verstärkte Expression neuronaler Markergene belegt werden. Verminderung der Expression von circSLC45A4 im embryonalen Mauscortex verursacht eine signifikante Reduktion von basalen Progenitoren. Außerdem wurde eine signifikante Reduktion von Zellen in der kortikalen Platte nach Depletion von circSLC45A4 gemessen. Weiterhin konnten die Ergebnisse im Mauscortex dekonvoliert werden. Dies zeigte die Zunahme von Cajal-Retzius Zellen. Es wird eine Methode vorgestellt, die RNA-Sequenzierung von Einzelzellen mit räumlicher Auflösung zulässt, ohne vorherige Kenntnisse des Systems zu benötigen. 3D-seq vereint die Applikation eines physischen Gitters mit kombinatorischem Indizieren, so dass Einzelzellen individuell und räumlich markiert werden können. 3D-seq wurde an koronalen Schnitten von adultem Mausgehirn etabliert. Die Daten wurden zur Reproduktion des Gewebes in silico genutzt. 3D-seq ein leicht zu adaptierendes Protokoll, das an jedem Gewebe angewendet werden kann. / Circular RNAs (circRNAs) are an endogenous class of animal RNAs. Despite their abundance, their function and expression in the nervous system are unknown. Therefore, a circRNA catalogue comprising RNA-seq samples from different brain regions, primary neurons, synaptoneurosomes, as well as during neuronal differentiation was created. Using these and other available data, thousands of neuronal human and mouse circRNAs were discovered and analyzed. CircRNAs were extraordinarily enriched in the mammalian brain, well conserved in sequence, often expressed as circRNAs in both human and mouse, and sometimes even detected in Drosophila brains. CircRNAs were overall upregulated during neuronal differentiation, highly enriched in synapses, and often differentially expressed compared to their corresponding mRNA isoforms. CircRNA expression correlated negatively with expression of the RNA-editing enzyme ADAR1. Knockdown of ADAR1 induced elevated circRNA expression. Together, a circRNA brain expression atlas and evidence for important circRNA functions is provided. Starting from this catalogue a circRNA, circSLC45A4 was identified. It is the main RNA isoform produced from its genetic locus in the developing human frontal cortex and one of the highest expressed circRNAs in that system. Knockdown of this conserved circular RNA in a human neuroblastoma cell line was sufficient to induce spontaneous neuronal differentiation, measurable by increased expression of neuronal marker genes and neurite outgrowth. Depletion of circSlc45a4 in the developing mouse cortex caused a significant reduction of the basal progenitor pool and increased the expression of neurogenic regulators like Notch2, Foxp2, and Unc5b. Furthermore, a significant depletion of cells in the cortical plate after knockdown of circSlc45a4 was observed. In addition, deconvolution of the bulk RNA-seq data with the help of single cell RNA-seq data validates the depletion of basal progenitors after knockdown of circSlc45a4 in the mouse cortex and reveals an increase in Cajal-Retzius cells. Taken together, a detailed study of a conserved circular RNA that is necessary to maintain the pool of neural progenitors in vitro and in vivo is presented. The developing mouse cortex is a good illustration for a highly spatially organized tissue and why knowledge of spatial information for each cell can be of great importance. However, obtaining transcriptome-wide and spatially resolved information from single-cells has been proven to be a challenging task. Current state-of-the-art experimental methods are either limited by the number of genes that can be detected simultaneously within a single-cell or require preexisting spatial information. Here, 3D-seq, a new experimental technique that allows unbiased, high-throughput single-cell spatial transcriptomics is introduced. 3D-seq combines a physical grid with combinatorial indexing to label single cells of any tissue in a unique way and thereby preserving the approximate spatial localization of any given cell. 3D-seq was applied to coronal slices of adult mouse brain, more than 70 cell types were identified and the 3D-seq data was used to reproduce the tissue in silico with single-cell resolution. Furthermore, 3D-seq is easy to adapt, can be applied to any tissue and can be combined with other technologies.

Fettvävnaden är en viktig regulator för ämnesomsättningen och uppvisar en komplex cellulär arkitektur som påverkar olika fysiologiska och patologiska processer. Dess heterogena natur är relativt ostrukturerad och består huvudsakligen av bräckliga feta adipocyter och immunceller. Dessa komplikationer försvårar studier av mikroarkitekturen - som är avgörande för att förstå dess beteende - vilket nyligen har gynnats av teknik med rumslig upplösning, som möjliggör studier av genomiska profiler samtidigt som informationen från vävnaden bevaras. I detta arbete undersöks kromatindynamiken i fettvävnad med hjälp av den nyutvecklade Spatial Assay for Transposase-Accessible Chromatin med sekvensering med hög genomströmning (Spatial ATAC-seq). Med fokus på subkutan vit fettvävnad samlades prover in från en individ som led av fetma före och fem år efter en bariatrisk operation för att studera förändringar i samband med betydande viktnedgång. Studien omfattar detaljer för både experimentella protokoll och avancerade beräkningsverktyg för dataanalys, inklusive användning av en utvecklingsversion av Semla-paketet för att integrera data om rumslig tillgänglighet och kromatintillgänglighet. Analysen visade på en mångsidig cellulär arkitektur och distinkta genomiska egenskaper i vävnaden, vilket framhävde förekomsten av specifika celltyper som AdipoLEP-liknande adipocyter och infiltrerande immunceller. Denna studie visade att det är möjligt att tillämpa Spatial ATAC-seq för att undersöka de molekylära mekanismerna i fettvävnad som ligger till grund för metabol hälsa och sjukdom, särskilt i samband med fetma och viktminskning. / Adipose tissue is a critical regulator of metabolism, exhibiting a complex cellular architecture that influences various physiological and pathological processes. Its heterogeneous nature is relatively unstructured, mainly formed by fragile fatty adipocytes and immune cells. These intricacies complicate the study of its microarchitecture – crucial for understanding its behaviour – which has recently benefitted from spatially resolved technologies, that enable the study of genomic profiles while keeping the information from the tissue. This work explores the chromatin dynamics of adipose tissue using the newly developed Spatial Assay for Transposase-Accessible Chromatin with high throughput sequencing (Spatial ATAC-seq). Focusing on subcutaneous white adipose tissue, samples were collected from an individual suffering from obesity before and five years after bariatric surgery to study changes associated with significant weight loss. The study comprises details for both experimental protocols and advanced computational tools for data analysis, including the use of a development version of Semla package to integrate spatial and chromatin accessibility data. The analysis revealed a diverse cellular architecture and distinct genomic features across the tissue, highlighting the presence of specific cell types such as AdipoLEP-like adipocytes and infiltrating immune cells. This study demonstrated the feasibility of applying Spatial ATAC-seq in investigating the molecular mechanisms of adipose tissue underlying metabolic health and disease, particularly in the context of obesity and weight loss.

The mortality rate is high for cancer patients and treatments are only efficient in a fraction of patients. To be able to cure more patients, new treatments need to be invented. Immunotherapy activates the immune system to fight against cancer and one treatment targets immune checkpoints. If more targets are found, more patients can be treated successfully. In this project, interactions between immune and cancer cells that drive tumour growth were investigated in an attempt to find new potential targets. This was achieved by creating a machine learning model that finds genes expressed in cells involved in tumour-driving interactions. Single-cell RNA sequencing and spatial transcriptomic data from breast cancer patients were utilised as well as single-cell RNA sequencing data from healthy patients. The tumour rate was based on the cumulative expression of G2/M genes. The G2/M related genes were excluded from the analysis since these were assumed to be cell cycle genes. The machine learning model was based on a supervised variational autoencoder architecture. By using this kind of architecture, it was possible to compress the input into a low dimensional space of genes, called a latent space, which was able to explain the tumour rate. Optuna hyperparameter optimizer framework was utilised to find the best combination of hyperparameters for the model. The model had a R2 score of 0.93, which indicated that the latent space was able to explain the growth rate 93% accurately. The latent space consisted of 20 variables. To find out which genes that were in this latent space, the correlation between each latent variable and each gene was calculated. The genes that were positively correlated or negatively correlated were assumed to be in the latent space and therefore involved in explaining tumour growth. Furthermore, the correlation between each latent variable and the growth rate was calculated. The up- and downregulated genes in each latent variable were kept and used for finding out the pathways for the different latent variables. Five of these latent variables were involved in immune responses and therefore these were further investigated. The genes in these five latent variables were mapped to cell types. One of these latent variables had upregulated immune response for positively correlated growth, indicating that immune cells were involved in promoting cancer progression. Another latent variable had downregulated immune response for negatively correlated growth. This indicated that if these genes would be upregulated instead, the tumour would be thriving. The genes found in these latent variables were analysed further. CD80, CSF1, CSF1R, IL26, IL7, IL34 and the protein NF-kappa-B were interesting finds and are known immune-modulators. These could possibly be used as markers for pro-tumour immunity. Furthermore, CSF1, CSF1R, IL26, IL34 and the protein NF-kappa-B could potentially be targeted in immunotherapy.

Le sommeil est un comportement qui occupe un tiers de notre vie. L'horaire, la durée, et la qualité du sommeil sont contrôlés par deux processus principaux : la régulation homéostatique du sommeil et l’horloge qui synchronise les rythmes circadiens internes. EPHA4 est une molécule d'adhésion cellulaire qui régule la neurotransmission et qui est exprimée dans des régions cérébrales impliquées dans la régulation circadienne et du sommeil. De manière intéressante, le gène EphA4 contient des éléments régulateurs des facteurs de transcription circadiens et les souris Clock mutantes voient leur expression d’EphA4 modifiée. De plus, les souris EphA4 knockout (KO) ont des rythmes circadiens d’activité locomotrice anormaux, moins de sommeil paradoxal dans la période de lumière, et une distribution des oscillations cérébrales du sommeil modifiée sur un cycle de 24 heures. Par conséquent, et étant donné que EPHA4 est crucial pour le neurodéveloppement, il convient d’explorer si les phénotypes du sommeil/circadiens observés chez les souris EphA4 KO proviennent d'effets sur le développement ou des rôles d'EPHA4 dans la fonction neuronale adulte. Par ailleurs, les mécanismes de régulation transcriptionnelle d'EphA4 sont encore méconnus. Dans cette thèse, nous avons émis les hypothèses que i) l'expression du gène EphA4 ou de leurs ligands Éphrines (Efns) est régulée de manière circadienne ; et ii) que le modulateur de l’activité d’EPHA4 rhynchophylline (RHY) modifie le sommeil chez les souris adultes d'une manière qui ressemble au phénotype EphA4 KO. L'étude I montre que les facteurs de transcription de l’horloge (CLOCK/NPAS2 et BMAL1) activent la transcription via les éléments de réponse à l'ADN «boîtes E» trouvées dans les promoteurs putatifs d'EphA4, EfnB2 et EfnA3 in vitro. Cependant, les protéines EPHA4 et EFNB2 n’ont pas montré une oscillation circadienne dans le cortex préfrontal et les noyaux suprachiasmatiques (horloge principale) de souris. Dans le projet II, l'effet de RHY sur le sommeil a été étudié chez des souris mâles et femelles avec des enregistrements electroencéphalographiques. Nos données ont démontré que RHY prolonge le sommeil à onde lente, mais les effets sur le sommeil paradoxal dépendent de l’heure d’injection. RHY modifie aussi les oscillations cérébrales pendant l’éveil et le sommeil. Tous ces effets sont notablement plus marqués chez les femelles, ce qui souligne l’importance d’étudier les deux sexes lors des essais pharmacologiques. La transcriptomique spatiale cérébrale révèle que RHY modifie des transcrits liés à des réponses d’inflammation dans tout le cerveau, mais qu'elle affecte l'expression génique des neuropeptides associés à la régulation du sommeil et hypophysaires particulièrement dans l’hypothalamus. En outre, RHY affecte l'expression des gènes de la transcription/traduction de manière diffèrent selon l’heure d’injection. La première publication met en évidence que la régulation transcriptionnelle d’EphA4 et des Efns pourraient expliquer quelques-uns des phénotypes observés chez les souris KO. La deuxième publication démontre que RHY induit le sommeil chez la souris et souligne l’importance de caractériser des mécanismes inexplorés sous-jacents aux composés naturels. Décrire la régulation moléculaire du sommeil peut apporter des éclairages utiles pour la chronopharmacologie. / Sleep is a behavior which occupies a third of our lifetime. The schedule, the duration and the quality of sleep are controlled by two main processes: the homeostatic sleep regulation and the clock that synchronizes the internal circadian rhythm. EPHA4 is a cell adhesion molecule regulating neurotransmission and is expressed in brain centers regulating sleep and circadian rhythms. Interestingly, the EphA4 gene contains regulatory elements for circadian transcription factors, and Clock mutant mice have altered EphA4 expression. Moreover, EphA4 knockout mice (KO) have abnormal circadian rhythms of locomotor activity, less paradoxical sleep in the light period and altered sleep brain oscillations across the 24 hours. Given that EPHA4 is crucial for development, it should be investigated whether the sleep/circadian phenotypes observed in EphA4 KO originate from developmental effects or from roles of EPHA4 in adult neuronal function. Moreover, very little is known about the transcriptional regulation of EPHA4. Thus, the hypotheses of this thesis were that i) the gene expression of EphA4 or that of its ligands Ephrins (Efns) is regulated in a circadian manner; and ii) that the modulator of EPHA4 activity rhynchophylline (RHY) modifies sleep in adult mice in manners that resemble the EphA4 KO phenotype. Project I demonstrates that the clock transcription factors (CLOCK/NPAS2 et BMAL1) activate transcription via the DNA regulatory elements “E-boxes” found in the putative promoters of EphA4, EfnB2 and EfnA3 in vitro. Nevertheless, EPHA4 and EFNB2 proteins did not show a circadian oscillation in the mouse prefrontal cortex and suprachiasmatic nuclei (master clock). In project II, the effect of RHY on sleep was studied in male and female mice with electroencephalographic recordings. RHY extends slow wave sleep and effects on paradoxical sleep depended on the time-of-injection. RHY also modified the brain oscillations during wakefulness and sleep. Importantly, all these effects were larger in females, which highlights the need to consider both sexes in pharmacological studies. Brain spatial transcriptomics reveals that RHY modifies transcripts linked to inflammatory responses throughout the brain, while it affects transcripts linked to sleep regulation and pituitary responses particularly in the hypothalamus. Moreover, RHY affected the expression of genes for transcription/translation differently depending on the time of injection. The first publication underscores that the transcriptional regulation of EphA4 and Efns may underly some of the phenotypes observed in the KO mice. The second publication demonstrates that RHY induces sleep in mice, that it modifies brain activity associated to cognitive processes and highlights the importance of characterizing unexplored mechanisms of natural compounds. Describing the molecular regulation of sleep may provide useful insights for chronopharmacology.