Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies

<p>Abstract</p> <p>Background</p> <p>In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes.</p> <p>In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets.</p> <p>Results</p> <p>We describe STEPath, a method that starts from gene expression profiles and integrates the analysis of imbalanced region as an <it>a priori </it>step before performing gene set analysis. The application of STEPath in individual studies produced gene set scores weighted by chromosomal activation. As a final step, we propose a way to compare these scores across different studies (meta-analysis) on related biological issues. One complication with meta-analysis is batch effects, which occur because molecular measurements are affected by laboratory conditions, reagent lots and personnel differences. Major problems occur when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. We evaluated the power of combining chromosome mapping and gene set enrichment analysis, performing the analysis on a dataset of leukaemia (example of individual study) and on a dataset of skeletal muscle diseases (meta-analysis approach).</p> <p>In leukaemia, we identified the Hox gene set, a gene set closely related to the pathology that other algorithms of gene set analysis do not identify, while the meta-analysis approach on muscular disease discriminates between related pathologies and correlates similar ones from different studies.</p> <p>Conclusions</p> <p>STEPath is a new method that integrates gene expression profiles, genomic co-expressed regions and the information about the biological function of genes. The usage of the STEPath-computed gene set scores overcomes batch effects in the meta-analysis approaches allowing the direct comparison of different pathologies and different studies on a gene set activation level.</p

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones

Cell Culture

Cell culture is cell cloning technology that simulates in vivo environment conditions such as asepsis, appropriate temperature, and pH as well as certain nutritional conditions to enable cells to survive, grow, reproduce, and maintain their structure and function. Cell culture can be used to grow human, animal, plant, and microbial cells. Each type of cell culture has its own characteristics and essential conditions. This book focuses on the advanced technology and applications of cell culture in the research and practice of medical and life sciences. Chapters address such topics as primary cancer cell cultures, 2D and 3D cell cultures, stem cells, nanotechnology, and more

Investigation of Neurexin 2 as a Candidate for Parkinson's Disease

Biomedical Sciences: Molecular Biology and Human Genetic

AMBRA1 as a Biomarker and its Functional Crosstalk with Autophagy and Epidermal Differentiation in Cutaneous Squamous Cell Carcinoma Tumourigenesis

Ph. D. ThesisCutaneous Squamous Cell Carcinoma (cSCC) is a skin cancer with an increasing worldwide incidence. While most patients have an excellent prognosis, a subset of patients develop disease recurrence/metastasis, emphasising the need for novel reliable prognostic biomarkers, as well as an improved understanding of the cellular signalling mechanisms underlying cSCC tumourigenesis and progression. Autophagy is essential for cellular homeostasis and keratinocyte differentiation, with the deregulation of both processes being associated with cSCC tumourigenesis. As a key protein to both autophagy and keratinocyte differentiation, the aim of the current study was to define crosstalk between AMBRA1 and the deregulation of these processes in cSCC development and progression and its potential, together with the associated autophagy cargo protein SQSTM1 (p62), as prognostic biomarkers. Biomarker assay development and analysis in a cohort of primary cSCC tumours revealed that loss of cytoplasmic AMBRA1 expression in the tumour growth front, in combination with loss of cytoplasmic p62 expression in the peritumoural epidermis, as putative prognostic biomarkers for cSCC reoccurrence and metastasis, independent of tumour differentiation status. Importantly, the combined loss of these proteins also identified moderately/poorly differentiated primary cSCC tumours at high risk of metastasis. Studies of the potential contribution of cullin E3 ligase-mediated degradation or TGF-β2-mediated downregulation of AMBRA1 in cSCC cell lines revealed only increased levels of TGF-β2 secretion correlated with loss of AMBRA1 expression. Furthermore, although chemical inhibition of TGF-β signalling inhibited cSCC cell proliferation in vitro, no effect on AMBRA1 expression levels was observed, suggesting an undefined TGF-β2 independent-mediated mechanism of AMBRA1 loss in cSCC. Studies investigating AMBRA1 involvement in keratinocyte differentiation and autophagy further demonstrated AMBRA1 expression in keratinocytes initially relies on autophagy activation but is later maintained by epidermal differentiation-related calcium signalling. Additional studies also revealed that this calcium-signalling mediated regulation of AMBRA1 expression is lost during cSCC tumourigenesis, likely resulting in the maintenance of a dedifferentiated cell phenotype, facilitating sustained tumour cell proliferation. This further highlights that loss of AMBRA1 expression as a key event in the uncoupling of autophagy and keratinocyte differentiation in cSCC development. Collectively these data highlight the tumour suppressive role of AMBRA1 in cSCC and its loss of expression in the tumour growth front, in combination with the loss of peritumoural epidermal p62 expression, as a novel prognostic biomarker for cSCC reoccurrence and metastasis.European Regional Development Fund, Northern Powerhouse and AMLo Biosciences Ltd