Information-theoretic sensitivity analysis: a general method for credit assignment in complex networks

Most systems can be represented as networks that couple a series of nodes to each other via one or more edges, with typically unknown equations governing their quantitative behaviour. A major question then pertains to the importance of each of the elements that act as system inputs in determining the output(s). We show that any such system can be treated as a ‘communication channel’ for which the associations between inputs and outputs can be quantified via a decomposition of their mutual information into different components characterizing the main effect of individual inputs and their interactions. Unlike variance-based approaches, our novel methodology can easily accommodate correlated inputs

Interactions and functionalities of the gut revealed by computational approaches

The gastrointestinal tract is subject of much research for its role in an organism’s health owing to its role as gatekeeper. The tissue acts as a barrier to keep out harmful substances like pathogens and toxins while absorbing nutrients that arise from the digestion of dietary components in in the lumen. There is a large population of microbiota that plays an important role in the functioning of the gut. All these sub-systems of the gastrointestinal tract contribute to the normal functioning of the gut. Due to its various functionalities, the gut is able to respond to different types of stimuli and bring the system back to homeostasis after perturbations. The work done in this thesis uses several bioinformatic tools to improve our understanding of the functioning of the gut. This was achieved with data from model animals, mice and pigs which were subjected to changing environments before their gastrointestinal response was measured. Different types of stimuli were studied (eg, antibiotic exposure, changing diets and infection with pathogens) in order to understand the response of the gut to varying environments. This data was analysed using different data integration techniques that provide a holistic view of the gut response. Vertical data integration techniques look for associations between different types of ~omics data to highlight possible interactions between the measured variables. Lateral integration techniques allow the study of one type of ~omics data over several time points or several experimental conditions. Using these techniques, we show proof of interactions between different sub-systems of the gut and the functional plasticity of the gut. Of the several hypotheses generated in this thesis we have validated several using existing literature and one using an in-vitro system. Further validation of these hypotheses will increase understanding of the responses of the gut and the interactions involved.</p

Identifying the drivers of inhibitor of apoptosis protein antagonist resistance in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the 7th leading cause of cancer related death. Deregulation of the apoptotic pathway, including upregulation of inhibitor of apoptosis proteins (IAPs), occurs at an early stage in PDAC resulting in aggressive tumours which are often resistant to therapy. Currently, use of IAP antagonists in clinical trials aims to sensitise cancer cells to apoptosis. However, these therapies show a lack of efficacy in a large proportion of the patient population. Therefore, elucidating the molecular mechanisms associated with resistance to IAP antagonists could optimise treatment strategies for PDAC. In this study, three different screening methodologies are used to identify novel resistance drivers to IAP antagonists. Firstly, a genome-wide CRISPR-Cas9 screen was conducted to identify 1025 potential genetic contributors (p<0.05) to birinapant response in the insensitive Suit-2 cell line. Secondly, two acquired resistance cell lines from Capan-1 and Panc-1 cell lines were created from prolonged treatment exposure. Differential gene expression analysis compared acquired resistance to parental cell lines to identify 494 common gene expression changes associated with resistance (P<0.05). Finally, large scale pan-cancer drug sensitivity data sets were analysed for multi-omic resistance drivers to four IAP antagonists: birinapant, LCL161, Embelin and AZD5582. This identified significant differences between IAP antagonists, with no common drivers identified for all antagonists. The three screening methods were compared to identify three birinapant resistance drivers present in all analyses: RSU1, ACVR2A and ANK2. From these findings, initial validations of several resistance drivers were conducted. These included investigations of single target knockdowns of MYC as well as the effects of ribosome biogenesis, Estrogen signalling and proteosome regulation on IAP antagonist sensitivity. Overall, cMYC silencing and BET inhibitor combinations were the most promising combinations with IAP antagonists, with significant synergies observed in several PDAC cell lines. In summary, this project successfully utilises three methodologies to uncover several novel IAP antagonist resistance drivers and suggests several rational combination targets to improve therapeutic strategies for pancreatic cancer.Open Acces

Applications of bioinformatics and machine learning in the analysis of proteomics data

In chapter one, a general introduction to the basic principles and techniques of MS-based proteomics, quantification strategies, and a generalized shotgun proteomics workflow are given. Moreover, I also outline how to analyze proteomics data from a bioinformatics perspective including normalization, dealing with missing values, differential analysis, functional annotation, as well as how to reveal the biology from post-translational modification data. Furthermore, I generalized the basics of machine learning algorithms from the perspective of supervised and unsupervised machine learning, along with that the application of machine learning algorithms to the identification of protein complexes. In chapter two, we are seeking to explore the drug addiction mechanism in melanoma cells that carry BRAF mutation. We present a proteomics and phosphoproteomics study of BRAFi-addicted melanoma cells (i.e., 451Lu cell line) in response to BRAFi withdrawal, in which ERK1, ERK2, and JUNB were genetically silenced separately using CRISPR-Cas9. We show that inactivation of ERK2 and, to a lesser extent, JUNB prevents drug addiction in these melanoma cells, while, conversely, knockout of ERK1 fails to reverse this phenotype, showing a response similar to that of control cells. Our data indicate that ERK2 and JUNB share comparable proteome responses dominated by the reactivation of cell division. Importantly, we find that EMT activation in drug-addicted melanoma cells upon drug withdrawal is affected by silencing ERK2 but not ERK1. Moreover, we reveal that PIR acts as an effector of ERK2, and phosphoproteome analysis reveals that silencing of ERK2 but not ERK1 leads to the amplification of GSK3 kinase activity. Our results depict possible mechanisms of drug addiction in melanoma, which may provide a guide for therapeutic strategies in drug-resistant melanoma. In chapter three, we are dedicated to exploring the role of PD-1 in T cell activation by comparing the proteome and phosphoproteome profiles in resting and activated CD8+ T cells, in which PD-1 was silenced using CRISPR–Cas9. Our data reveal that the activated T cells reprogrammed their proteome and phosphoproteome marked by activating of mTORC1 pathway. Moreover, we find that silencing of PD-1 altered the expression of E3 ubiquitin-- protein ligases, and increased glucose and lactate transporters. On the phosphoproteomics level, it evokes phosphorylation events in the mTORC1 pathway and activates the epidermal growth factor and its downstream MAPK pathway. Therefore, the data presented in this chapter depicts mechanisms of PD-1 in response to TCR stimulation in CD8+ T cells, which may provide a guide in immune homeostasis and immune checkpoint therapy. In chapter four, we construct a comprehensive map of human protein complexes through the integration of protein-protein interactions and protein abundance features. A deep learning framework was built to predict protein-protein interactions (PPIs), followed by a two-stage clustering to identify protein complexes. Our deep learning technique-based classifier significantly outperformed recently published machine learning prediction models with an F1-measure of 0.68 and captured in the process 5,010 complexes containing over 9,000 unique proteins. Moreover, this deep learning model enables us to capture poorly characterized interactions and the co-expressed protein involved interactions

Exploring Genetic Susceptibility: Using a combined systems biology, in vitro and ex vivo approach to understand the pathology of ulcerative colitis

The overall aim of this PhD is to use a multidisciplinary approach to determine the function of Ulcerative Colitis (UC) associated SNPs, to help understand the role of SNPs in the pathogenesis of UC in general and in a patient specific context. UC is a chronic, relapsing inflammatory disease of the large bowel for which the aetiology is thought to be a trifecta of 1) dysregulation of the immune system in response to 2) an environmental trigger in a 3) genetically susceptible host. Genetic susceptibility or susceptibility loci for UC have been identified by Genome Wide Associations Scanning and subsequent fine mapping and deep sequencing. This work intended to further characterise these susceptibility loci at a global level and a patient specific level using both a systems biology approach and experimental validation of the in-silico work. Using publicly available datasets non exonic UC associated SNPs were functionally annotated to regulatory regions within the genome. Exonic SNPs were also analysed looking at impacts in protein linear motifs and splice enhancement motifs. Bioinformatics was used to identify interacting proteins and create a UC-interactome network. This suggested that UC was a disease of fine regulators as opposed to a disease of specific target proteins. Analysis of the UC-interactome identified the focal adhesion complex (FAC) that is involved in regulating wound healing as major component of the network. One member of the FAC, Leupaxin (LPXN), was identified as a potential target for validation. Using CRISPR-Cas9 technology, LPXN overexpressing cell lines and knock out cell lines were created. Wound healing assays and cytokine analysis identified that overexpression of LPXN impaired wound healing and reduced the secretion of MCP-1. In addition, using genotyped colonic biopsies from UC patients and control patients in a polarised in vitro organ culture (pIVOC) system we show that the LPXN risk allele may impact on cytokine production. Finally, UKIBD genetics consortium data was used to access a pilot dataset of 58 patients’ SNP profiles from Immunochip data who were patients at the Norfolk and Norwich University Hospital to create patient-specific UC-interactomes. Analysis of these footprints identified convergent interacting proteins affected by multiple SNPs and novel pathogenic pathways

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions

The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

Spatial and genomic analysis of the glioblastoma tumor microenvironment

Glioblastoma (GBM) is an aggressive brain cancer with devastating outcomes and few effective treatments. Although immunotherapy has shown promise in treating a variety of cancers, it is still unclear if and how it can be effectively used in GBM. Elucidating this will require a better understanding of the mechanistic role of immune cells and their interactions in the GBM tumor microenvironment. This thesis utilizes recent technological developments in cancer genomics and imaging to study the mechanisms underlying immunotherapy and the tumor microenvironment. First, we will provide background on our current understanding of GBM, its immune microenvironment, as well as modern sequencing and imaging methods. Second, we will present a longitudinal study of GBM patients before and after treatment with PD-1 immunotherapy. Only a small fraction of GBM patients respond to this type of therapy, so we perform genomic, transcriptomic, and spatial analyses to compare the molecular features of these rare responders versus non-responders. We show that clinical response to PD-1 immunotherapy in GBM is associated with specific molecular alterations and immune infiltration profiles that reflect the tumor’s clonal evolution during treatment. The most common infiltrating immune cells in GBM are macrophages, which are implicated in a wide variety of pro-tumor and anti-tumor roles. We then focus on this specific immune population by analyzing single-cell expression data from GBM tumors. We identify a novel macrophage subpopulation characterized by expression of the scavenger receptor MARCO, which drives tumor progression in GBM and is altered over the course of PD-1 immunotherapy. Next, we demonstrate that the methods we have developed for GBM are applicable to understanding the tumor microenvironments of other cancers as well. We analyze a cohort of melanoma cases to show that transcriptomic and imaging features can be combined to create a biomarker that stratifies patients into different risk groups. Finally, while most of the image analysis described so far has utilized histopathology, we include two appendices where we demonstrate new ways to process and analyze Magnetic Resonance Imaging (MRI) in GBM