Inflammation is considered as one of the main defence mechanisms of the immune system against threats that occur in the body. When present in its acute form, minimal or no detectable subsequent damage of original affected tissue exists. The more pathological form, chronic inflammation, is associated with permanent damage of the tissue and typically a hallmark of various diseases such as ulcerative colitis or colon carcinogenesis. These two pathologies are evolving in the unique colon microenvironment, where intensive interaction between the host cells and bacteria is present. The aim of our study was to investigate the immunological (ELISA, FACS, RT-PCR) and structural (histology, confocal microscopy) changes in the colon mucosa of Wistar-AVN rats induced by dextran sodium sulphate (DSS) to produce colon colitis and by azoxymethane (AOM) to produce colon carcinogenesis. Conventional (CV) and also germ-free (GF) reared animals were used to investigate the effects of the mucosal inflammation activated by the administered inducers as well as the role of colon microbiota - as promoters of a continuous immune activation - in the modulation of immunity and collagen scaffold remodelling. Our results showed that even in the early period after the induction, both inducers produced a smouldering...

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.

Class I PI3Ks are a family (α, β, δ and γ) of ubiquitous lipid kinases that can be activated by cell surface receptors to 3-phosphorylate PI(4,5)P2 (phosphatidylinositol(4,5)-bisphosphate) and generate the signalling lipid PI(3,4,5)P3. The PI(3,4,5)P3 signal then activates a diverse collection of effector proteins involved in regulation of cell migration, metabolism and growth. The importance of this network is evidenced by the relatively high frequency with which cancers acquire gain-of-function mutations in this pathway and huge efforts to make PI3K inhibitors to treat cancer. The canonical model describing these events suggests class I PI3Ks are activated at the plasma membrane and generate PI(3,4,5)P3 in the inner leaflet of the plasma membrane where its effectors are activated. The PI(3,4,5)P3 signal can be terminated directly, by the tumour-suppressor and PI(3,4,5)P3-3-phosphatase PTEN, or modified to a distinct PI(3,4)P2 signal, by SHIP-family 5-phosphatases. The PI(3,4)P2 is removed by INPP4-family 4-phosphatases. Published work has shown that PI(3,4,5)P3 signalling can also occur in endosomes and nuclei, however, there is very little data defining the intracellular distribution of endogenous class I PI3Ks that supports these ideas; this is as a result of technical problems such as; their very low abundance, poor antibody-based tools and artefacts generated by overexpression of PI3Ks. Past work has indicated that, in PTEN-null mouse models of prostate tumour progression, either PI3Kβ or PI3Ks α and β, have important roles. Furthermore, the cell types and mechanism involved remained unclear. Recent published work in the host laboratory had indicated that there is an unexpectedly large accumulation of PI(3,4)P2 in PTEN-null cells that might be an important part of its status as a major tumour suppressor. The explanation and prevalence of this observation was unclear but potentially a result of PTEN also acting as a PI(3,4)P2 3-phosphatase in vivo. MEFs were derived from genetically-modified mice expressing endogenous, AviTagged class I PI3K subunits and used in experiments to define the subcellular localisation of class I PI3Ks. We found that following stimulation with PDGF, class IA PI3K subunits were unexpectedly depleted from the adherent basal membrane, in contrast, p85α and p110α, but not p85β and p110β, accumulated transiently in the nucleus. Interestingly, p110β, but none of the other subunits, was constitutively localised in the nucleus. These results support the idea that class I PI3K and PI(3,4,5)P3 signalling occurs in the nucleus. In organoids derived from WT, PI3Kγ-null or PTEN-null mouse prostate, application of PI3K-selective inhibitors revealed that PI3Kα had a dominant role in generating PI(3,4,5)P3 in prostate epithelial cells. The levels of PI(3,4)P2 were also elevated substantially in PTEN-null, compared to WT prostate organoids, use of PI3K-selective inhibitors suggested that it was also generated by PI3Kα. These data were consistent with the idea that PTEN can act as a PI(3,4)P2 3-phosphatase. Surprisingly, raising the pH of the organoids medium dramatically increased accumulation of PI(3,4,5)P3 and PI(3,4)P2, although the cause of this effect was unclear, we hypothesised the pH of the local environment may influence signalling via class I PI3Ks.