Proteogenomic characterization of hepatocellular carcinoma

We performed a proteogenomic analysis of hepatocellular carcinomas (HCCs) across clinical stages and etiologies. We identified pathways differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. These pathways are involved in the organization of cellular components, cell cycle control, signaling pathways, transcriptional and translational control and metabolism. Analyses of CNA-mRNA and mRNA-protein correlations identified candidate driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β- catenin pathway, transcriptional control, cholesterol biosynthesis and sphingolipid metabolism. The activity of targetable kinases aurora kinase A and CDKs was upregulated. We found that CTNNB1 mutations are associated with altered phosphorylation of proteins involved in actin filament organization, whereas TP53 mutations are associated with elevated CDK1/2/5 activity and altered phosphorylation of proteins involved in lipid and mRNA metabolism. Integrative clustering identified HCC subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our analysis provides insights into the molecular processes underlying HCCs

Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology

Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data. The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups. This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base. However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects. Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I Abstract ... V Acknowledgements ... VII Prelude ... IX 1 Introduction 1.1 An overview of environmental toxicology ... 2 1.1.1 Environmental toxicology ... 2 1.1.2 Chemicals in the environment ... 4 1.1.3 Systems biological perspectives in environmental toxicology ... 7 Computational toxicology ... 11 1.2.1 Omics-based approaches ... 12 1.2.2 Linking chemical exposure to transcriptional effects ... 14 1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19 1.2.4 Biomedical literature-based discovery ... 24 1.2.5 Deep learning with knowledge representation ... 27 1.3 Research question and approaches ... 29 2 Methods and Data ... 33 2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34 2.1.1 Exposure and microarray data ... 34 2.1.2 Preprocessing ... 35 2.1.3 Differential gene expression ... 37 2.1.4 Association rule mining ... 38 2.1.5 Weighted gene correlation network analysis ... 39 2.1.6 Method comparison ... 41 Predicting exposure-related effects on a molecular level ... 44 2.2.1 Input ... 44 2.2.2 Input preparation ... 47 2.2.3 Deep learning models ... 49 2.2.4 Toxicogenomic application ... 54 3 Method comparison to link complex stream water exposures to effects on the transcriptional level ... 57 3.1 Background and motivation ... 58 3.1.1 Workflow ... 61 3.2 Results ... 62 3.2.1 Data preprocessing ... 62 3.2.2 Differential gene expression analysis ... 67 3.2.3 Association rule mining ... 71 3.2.4 Network inference ... 78 3.2.5 Method comparison ... 84 3.2.6 Application case of method integration ... 87 3.3 Discussion ... 91 3.4 Conclusion ... 99 4 Deep learning prediction of chemical-biomolecule interactions ... 101 4.1 Motivation ... 102 4.1.1Workflow ...105 4.2 Results ... 107 4.2.1 Input preparation ... 107 4.2.2 Model selection ... 110 4.2.3 Model comparison ... 118 4.2.4 Toxicogenomic application ... 121 4.2.5 Horizontal augmentation without tail-padding ...123 4.2.6 Four-class problem formulation ... 124 4.2.7 Training with CTD data ... 125 4.3 Discussion ... 129 4.3.1 Transferring biomedical knowledge towards toxicology ... 129 4.3.2 Deep learning with biomedical knowledge representation ...133 4.3.3 Data integration ...136 4.4 Conclusion ... 141 5 Conclusion and Future perspectives ... 143 5.1 Conclusion ... 143 5.1.1 Investigating complex mixtures in the environment ... 144 5.1.2 Complex knowledge from literature and curated databases predict chemical- biomolecule interactions ... 145 5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146 5.2 Future perspectives ... 147 S1 Supplement Chapter 1 ... 153 S1.1 Example of an estrogen bioassay ... 154 S1.2 Types of mode of action ... 154 S1.3 The dogma of molecular biology ... 157 S1.4 Transcriptomics ... 159 S2 Supplement Chapter 3 ... 161 S3 Supplement Chapter 4 ... 175 S3.1 Hyperparameter tuning results ... 176 S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179 S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183 S3.4 Horizontal augmentation without tail-padding ... 183 S3.5 Four-relationship classification ... 185 S3.6 Interpreting loss observations for SemMedDB trained models ... 187 List of Abbreviations ... i List of Figures ... vi List of Tables ... x Bibliography ... xii Curriculum scientiae ... xxxix Selbständigkeitserklärung ... xlii

A systems biology approach to musculoskeletal tissue engineering: transcriptomic and proteomic analysis of cartilage and tendon cells

Disorders of cartilage and tendon account for a high incidence of disability and are highly prevalent co-morbidities within the ageing population; therefore, musculoskeletal disorders represent a major public health policy issue. Despite considerable efforts to characterise biochemical and biomechanical cues that promote a stable differentiated cartilage or tendon phenotype in vitro the benchmarks by which progress is measured are limited. Common regenerative interventions, such as autologous cartilage implantation, have a required period of monolayer expansion that induces a loss of the functional phenotype, termed dedifferentiation. Dedifferentiation has no definitive mechanism yet is widely described in both regenerative and degenerative contexts; in addition to stem cell transplantation and cell-seeding in three-dimensional scaffolds, dedifferentiation represents the third approach to the development of regenerative mechanisms for mammalian tissue repair. Cartilage and tendon show a number of common features in structure, develop, disease, and repair. The extracellular matrix is a dynamic and complex structure that confers the functional mechanical properties of cartilage and tendon. Dysregulation of production and degradation are critical to the pathophysiology of musculoskeletal disorders, therefore, reparative interventions require a stable, functional phenotype from the outset. Cartilage and tendon demonstrate a commonality in terms of function defining structure both being sparsely cellular with a preponderance of collagenous matrix. Parity of functionality with the pre- injury state after healing is rarely achieved for cartilage and tendon. Cartilage and  tendon also share common embryological origins. Common mesenchymal progenitor cells differentiate into many musculoskeletal tissues with diverse functions. Specialist sub-populations of tendon and cartilage progenitors enable formation of transitional zones between these developing tissues. The development of musculoskeletal structures does not occur in isolation, however, cartilage and tendon have not previously been considered together in a systems context. An integrated understanding of the differentiation of these tissues should inform regenerative therapies and tissue engineering strategies. Systems biology is paradigm shift in scientific thinking where traditional reductionist strategies to complex biological problems have been superseded by a holistic philosophy seeking to understand the emergent behavior of a system by the integrative and predictive modeling of all elements of that system. Whole transcriptome and proteome profiling studies are used to collect quantitative data about a system, which may then be exploited by systems biology methodologies including the analysis of gene and protein networks. Gene-gene co-expression relationships, which are core regulatory mechanisms in biology, are often not part of a comprehensive gene expression analysis. Many biological networks are sparse and have a scale-free topology, which generally indicates that the majority of genes have very few connections, whilst certain key regulators, or ‘hubs’, are highly interconnected. Co-expression networks may be used to define regulatory sub- networks and ‘hubs’ that have phenotypic associations. This approach allows all quantitative data to be used and makes no a priori assumptions about relationships in the system and, therefore, can facilitate the exploration of emergent behavior in the system and the generation of novel hypotheses. The ultimate goal of tissue engineering is the replacement of lost or damaged cells, and in vitro, to develop biomimetic (organotypic) structures to serve as experimental models. Tissues, and the strategies to functionally replicate them ex vivo, are complex and require an integrated, multi-disciplinary approach. Systems biology approaches, using data arising from multiple-levels of the biological hierarchy, can facilitate the development of predictive models for bioengineered tissue. The iterative refinement, quantification, and perturbation of these models may expedite the translation of well-validated organotypic systems, through legal regulatory frameworks, into regenerative strategies for musculoskeletal disorders in humans. In this thesis the systems under consideration are the major cell populations of cartilage and tendon (chondrocytes and tenocytes, respectively). They are described in three environmental conditions: native tissue, monolayer (two- dimensional), or three-dimensional models. There has been no systematic investigate of the global gene and protein profiles of cartilage and tendon in their native state relative to monolayer or three-dimensional cultures. There is no clear mechanistic description of the impact of in vitro environmental perturbations on the system or indeed the adequacy of these models as proxies for cartilage and tendon. A discovery approach using transcriptomic and proteomic profiling is undertaken to define a robust and consistent gene and protein profile for each condition. Differentially expressed elements are functionally annotated and pathway topology approaches employed to predict major signalling pathways associated with the observed phenotype. This study defines dedifferentiated chondrocytes and tenocytes in monolayer culture as expressing markers of musculoskeletal development, including scleraxis (Scx) and Mohawk (Mkx). Furthermore, there is reproducible synthetic profile convergence in monolayer culture between cartilage and tendon cells. Standard three-dimensional culture systems for chondrocyte and tenocytes fail to replicate the gene expression profile of cartilage and tendon. The PI-3K/Akt signaling pathway is predicted to be the predominant canonical pathway associated with de- and re-differentiation in vitro. Using novel, and publically available, transcriptomic data sets a meta-analysis of microarray gene expression profiles is performed using weighted gene co- expression network analysis. This is employed for transcriptome network decomposition to isolate highly correlated and interconnected gene-sets (modules) from gene expression profiles of cartilage and tendon cells in different environmental conditions. Sub-networks strongly associated with de- and re- differentiation phenotypes are defined. Comparison of global transcriptome network architecture was performed to define the conservation of network modules between a model species (rat) and human data. In addition to the annotation of an osteoarthritis-associated module in the rat a class-prediction analysis defined a minimal gene signature for the prediction of three-dimensional cultures from standard monolayer culture. Finally, proteomic and transcriptomic data sets are integrated by defining common upstream regulators (TGFB and PDGF BB) and unified mechanistic networks are generated for de- and re- differentiation. The studies collected in this thesis contribute to a wider understanding of cartilage and tendon tissue engineering and organotypic culture development. A clear mechanistic understanding of the regulatory networks controlling differentiation of cartilage and tendon progenitor cells is required in order to develop improved in vitro models and bio-engineered tissue that are physiologically relevant. The findings presented here provide practical outputs and testable hypotheses to drive future evidence-based research in organotypic culture development for musculoskeletal tissues

Covid-19: Perspectives Across Africa

This book emanated from the Society for the Advancement of Science in Africa's (SASA) Seventh Annual International (digital) Conference: Joint SASA and Ugandan Ministry of Health October 15, 2020 – January 14, 2021, Kampala, Uganda. The chapters in this book were solicited from presenters and also from other authors familiar with the impact of Covid-19 in Africa. There are 21 chapters, all together offering a range of perspectives from a variety of angles.SASA (Society for the Advancement of Science in Africa

Effects of Complementary use of Organic and Inorganic fertilizers on the growth and yield of Cucumber (Cucumu sativus. L.) on an ultisol

A field study was conducted in 2008 and 2009 early cropping seasons to assess the response of cucumber (Cucumus sativus L.) to complementary use of organic and inorganic fertilizers in Uyo agro-ecology. The fertilizer treatments were: NPK (15:15:15) at 100 and 200 kgha-1, poultry manure (PM) at 5 and 10 tha-1 , and complementary application of 100 kgha-1 of NPK + 5 tha-1 of PM, 100 kgha-1 of NPK + 10 tha-1 of PM, 200 kgha1 of NPK +5 tha-1 of PM ,200 kgha-1 of NPK +10 tha-1 of PM and control (no fertilizer). Results showed significant differences (P<0.05) in all the growth and yield parameters considered in both cropping seasons. The combined application of 200 kgha-1 of NPK and 10 tha-1 of PM performed better than sole application of either organic or inorganic fertilizer, with fresh fruit yield of 14.63 and 14.92 tha-1 in 2008 and 2009, respectively and exceeded other treatments by 1 -76% and 1-73% in 2009 and 2010, respectively. This indicates strongly the synergistic benefits of using both organic and inorganic fertilizers even at lower rates