A hallmark of the joint disease osteoarthritis (OA) is the degradation of the articular cartilage in the affected joint, debilitating pain and decreased mobility. At present there are no disease modifying drugs for treatment of osteoarthritis. This represents a significant, unmet medical need as there is a large and increasing prevalence of OA. Using a systems biology approach, we aimed to better understand the pathogenic mechanisms of OA and ultimately aid development of therapeutics. This thesis focuses on the analysis of gene expression data from human OA cartilage obtained at total knee replacement (TKR). This transcriptomics approach gives a genome-wide overview of changes, but can be challenging to interpret. Network-based algorithms provide a framework for the fusion of knowledge so allowing effective interpretation. The PhenomeExpress algorithm was developed as part of this thesis to aid the interpretation of gene expression data. PhenomeExpress uses known disease gene associations to identify relevant dysregulated pathways in the data. PhenomeExpress was further developed into an 'app' for Cytoscape, the widely used network analysis and visualisation platform. To investigate the processes that occur during the degradation of cartilage we examined the gene expression of damaged and intact OA cartilage using RNA-Seq and identified key altered pathways with PhenomeExpress. A regulatory network driven by four transcription factors accounts for a significant proportion of the observed differential expression of damage-associated genes in the PhenomeExpress identified pathways. We further explored the role of the cytokines IL-1 and TNF that have been reported to β drive the progression of OA. Comparison of the expression response of in vitro cytokine-treated explants with the in vivo damage response revealed major differences, providing little evidence for any significant role of IL-1 and TNF as drivers of OA β damage in vivo. Finally, we examined the heterogeneity of OA through analysis of cartilage expression profiles at TKR. Through a network-based clustering method, we found two subgroups of patients on the basis of their gene expression profiles. These subgroups were found to have distinct OA expression perturbations and we identified TGF and S100A8/9 β signalling as potentially explaining the observed differential expression. We developeda RT-qPCR based classifier that allowed classification of new samples into these subgroups so allowing future assessment of the clinical significance of these subgroups. The work presented in this thesis includes a novel, widely-accessible tool for the analysis of disease gene expression data, which we used to give new insights into the pathogenesis of osteoarthritis. We have produced a rich dataset for future research and our analysis of this data has increased our understanding of cartilage damage processes and the heterogeneity of OA.

Cell division is one of the most fundamental processes of life whereby one cell replicates itself to produce two. The molecular machinery that drives and regulates this fundamental process has been much studied but much remains unknown. This work describes the use of transcriptomics analyses to identify putative new proteins involved with this process and subsequent attempts to prove their association with this pathway. Using the latest array technology, in Chapter 2 I describe studies that examine the expression of genes regulated during different stages of the human cell cycle. Synchronous populations of neonatal human dermal fibroblasts (NHDFs) were generated by serum starvation and analysed in two separate microarray experiments. For the first set array experiments, samples were taken every 6 hours for 48 hours after serum refeeding, and every 2 hours for 24 hours for the second experiment. Using BioLayout Express3D, network structure analyses identified four major clusters of gene expression patterns associated with different stages of the cell cycle: G0-, early G1-, late G1-, and S/G2/M-phase. By comparison with datasets of other human cells and tissues, the list of genes in the S/G2/M cluster was refined; genes were only kept in the list if they were found to be co-expressed in cells and tissues with high levels of cell proliferation. 706 genes that were co-expressed during S/G2/M-phase were selected for further analyses. Manual curation showed that 484 are known cell cycle-associated genes, 78 are genes with putative association to the cell cycle, and 75 have known roles in other biological processes, whilst 69 were entirely uncharacterised genes. In order to investigate the 69 genes with unknown function, in Chapter 3 I describe how RNAi was used to screen 42 of these genes to see if their knockdown resulted in an effect on cell proliferation. After extensive assay optimisation, endoribonuclease-prepared siRNA (esiRNA) was delivered to NHDF cells and the effect of knockdown determined using a real time cell analysis (RTCA) system. This system monitors the change in electrical resistance induced by growing cell populations defined as the cell impedance index (CI). Using a Z-scoring cut-off to determine the hits of the RNAi screening, according to the average value of cell impedance growth rate (CIGR i.e. a value from transformed CI), 19 of 42 genes were found to significantly affect the dynamics of cell proliferation, supporting a potential role in cell division. In order to verify that the unknown proteins localise to structures compatible with a role in the cell cycle, in Chapter 4 I describe protein localisation studies on 11 of 19 genes of ‘hits’ from Chapter 3 (we were unable to obtain clones for the other 8 genes) and other genes of interest. Transfection studies of HEK293T cells with expression clones containing more than 11 ORFs with GFP fused to either the N- or C-terminal were performed. FAM111B and KIAA1549L appeared to be localised to the centrosome. In order to better understand the context in which the novel centrosomal proteins that FAM111B might operate, in Chapter 5 I describe the construction of a large-scale pathway model of centrosome life cycle based on an extensive literature review. The model is composed of 117 of the most important centrosome-associated proteins and has been constructed using the modified Edinburgh Notation (mEPN) scheme. This model was used to better annotate the genes in the original S/G2/M list and understand which of the genes in the model are regulated during cell division. This regulatory network model of the centrosome life cycle represents an important summary of current knowledge and provides a useful resource for further analyses of the novel centrosomal proteins. In summary, a list cell cycle gene was derived from microarray experiments by using network structure analyses. Subsequent analyses filtered the genes that co-expressed during S/G2/M-phase narrowing down into 706 genes. Of this list, 69 genes had not previously been associated with the cell cycle. 42 of these unknown genes were analysed by using real time RNAi screening, 19 of these genes were indeed associated with the cell proliferation, and 2 of these genes with unknown function appear to localise to the centrosome. To predict their involvement in the centrosome life cycle, a pathway map composed of 117 centrosome-associated proteins were formed. Although further research is needed to determine their position in the centrosome life cycle, the pathway can be used for computational modelling testing their putative function in the system.

abstract: The greatest barrier to understanding how life interacts with its environment is the complexity in which biology operates. In this work, I present experimental designs, analysis methods, and visualization techniques to overcome the challenges of deciphering complex biological datasets. First, I examine an iron limitation transcriptome of Synechocystis sp. PCC 6803 using a new methodology. Until now, iron limitation in experiments of Synechocystis sp. PCC 6803 gene expression has been achieved through media chelation. Notably, chelation also reduces the bioavailability of other metals, whereas naturally occurring low iron settings likely result from a lack of iron influx and not as a result of chelation. The overall metabolic trends of previous studies are well-characterized but within those trends is significant variability in single gene expression responses. I compare previous transcriptomics analyses with our protocol that limits the addition of bioavailable iron to growth media to identify consistent gene expression signals resulting from iron limitation. Second, I describe a novel method of improving the reliability of centroid-linkage clustering results. The size and complexity of modern sequencing datasets often prohibit constructing distance matrices, which prevents the use of many common clustering algorithms. Centroid-linkage circumvents the need for a distance matrix, but has the adverse effect of producing input-order dependent results. In this chapter, I describe a method of cluster edge counting across iterated centroid-linkage results and reconstructing aggregate clusters from a ranked edge list without a distance matrix and input-order dependence. Finally, I introduce dendritic heat maps, a new figure type that visualizes heat map responses through expanding and contracting sequence clustering specificities. Heat maps are useful for comparing data across a range of possible states. However, data binning is sensitive to clustering cutoffs which are often arbitrarily introduced by researchers and can substantially change the heat map response of any single data point. With an understanding of how the architectural elements of dendrograms and heat maps affect data visualization, I have integrated their salient features to create a figure type aimed at viewing multiple levels of clustering cutoffs, allowing researchers to better understand the effects of environment on metabolism or phylogenetic lineages. / Dissertation/Thesis / Chapter 2 Excel file of transcriptome responses / Chapter 2 Perl scripts / Chapter 3 Cluster Aggregation Perl script / Chapter 4 Example of the top-down clustering method used to construct dendritic heat maps / Chapter 4Perl scripts and dendritic heat map images / Chapter 4 Perl scripts and dendritic heat map images / Doctoral Dissertation Geological Sciences 2017

O mexilhão marrom Perna perna (Linnaeus, 1758) auxilia no monitoramento de compostos químicos em ecossistemas marinhos. No entanto, os mecanismos moleculares de detoxificação e resposta ao estresse são desconhecidos. Elucidar esses mecanismos é crucial para entender os efeitos tóxicos dos poluentes químicos e desenvolver biomarcadores para avaliar a qualidade ambiental dos ecossistemas marinhos. No presente estudo, indivíduos da espécie P. perna foram expostos a antraceno (ANT) e os RNAs mensageiros (mRNA) das brânquias foram sequenciados com a plataforma Illumina. A análise química do tecido mole dos animais identificou concentrações de ANT 268 a 715 vezes mais alta no grupo exposto comparado ao grupo controle, demonstrando que a exposição foi realizada com sucesso. O sequenciamento do transcritoma do P. perna gerou 273.152.390 pares de reads, resultando na montagem de 231.728 contigs com tamanho médio de 720 pb e N50 de 1.083 pb, os quais 66.563 contigs (28,7%) pode ser anotado utilizando banco de dados como GenBank, Pfam, Gene Ontology e KEGG. Os resultados obtidos a partir da anotação funcional sugerem que as brânquias tenham papel na biotransformação de xenobióticos, resposta antioxidante, sinalização, resposta imunológica inata, e osmorregulação. Foi possível identificar genes de biotransformação de fase I, II e III, incluindo CYPs e GSTs. Transcritos similares a CYPs e GSTs estavam sendo expressos no grupo exposto, porém nenhum deles foram classificados como diferencialmente expressos. Contudo, muitos genes hipotéticos foram diferencialmente expressos, o que sugere que P. perna utilize mecanismos desconhecidos de biotransformação para lidar com a contaminação de ANT. Genes de sistema imune inato foram regulados tanto positivamente quanto negativamente, assim como observado para Perna viridis exposto a benzo(a)pireno, sugerindo que ANT promove alterações da capacidade de resposta do sistema imune inato do P. perna. / The brown mussel Perna perna (Linnaeus, 1758) helps the monitoring of chemical compounds in marine ecosystems. However its molecular mechanisms of detoxification and stress response remain unclear. Elucidating these mechanisms is crucial to understand the toxic effects of chemical pollutants and to develop biomarkers to assess marine ecosystems. In this study, P. perna individuals were exposed to anthracene (ANT) and its mRNA complement was sampled sequenced with Illumina technology. Chemical analysis of the soft tissue identified ANT concentrations 268 - 715 fold higher in the exposed group compared to controls, demonstrating that the exposure procedure was successfully accomplished. Transcriptome sequencing of P. perna generated 273.152.390 paired reads that were assembled in 231.728 contigs of average length 720 bp and N50 1.083 bp , which 66.563 contigs (28,7%) could be annotated using GenBank genes, Pfam domains, Gene Ontology (GO) terms and KEGG pathways. The results obtained from functional annotation suggest gills play a role in xenobiotics biotransformation, antioxidant response, signal transduction, innate immune response, and osmoregulation. It was possible to identify transcripts similar to genes related with biotransformation reactions of phases I, II and III, including CYPs and GSTs. Transcripts similar to CYPs and GSTs isoforms were highly expressed in the group exposed to ANT, however no CYP, GST, or even other genes related with biotransformation reactions were classified as differentially expressed. On the other hand, several hypothetical genes were differentially expressed, which suggests that P. perna uses unknown mechanisms of biotransformation to deal with ANT stress contamination. Immune related-genes were both up and down-regulated, as was also observed for Perna viridis exposed to benzo(a)pyrene, suggesting that ANT promotes alteration in the immune response of P. perna.

Philosophiae Doctor - PhD / Apple (Malus x domestica) production in the Western Cape, South Africa, is one of the major contributors to the gross domestic product (GDP) of the region. The production of apples is affected by a number of diseases. One of the economically important diseases is apple scab that is caused by the pathogenic fungus, Venturia inaequalis. Research to introduce disease resistance ranges from traditional plant breeding through to genetic manipulation. Parallel disease management regimes are also implemented to combat the disease, however, such strategies are increasingly becoming more ineffective since some fungal strains have become resistant to fungicides. The recently sequenced apple genome has opened the door to study the plant pathogen interaction at a molecular level. This study reports on proteomic and transcriptomic analyses of apple seedlings infected with Venturia inaequalis. In the proteomic analysis, two-dimensional gel electrophoresis (2-DE) in combination with mass spectrometry (MS) was used to separate, visualise and identify apple leaf proteins extracted from infected and uninfected apple seedlings. Using MelanieTM 2-DE Gel Analysis Software version 7.0 (Genebio, Geneva, Switzerland), a comparative analysis of leaf proteome expression patterns between the uninfected and infected apple leaves were conducted. The results indicated proteins with similar expression profiles as well as qualitative and quantitative differences between the two leaf proteomes. Thirty proteins from the apple leaf proteome were identified as differentially expressed. These were selected for analysis using a combination of MALDI-TOF and MALDI-TOF-TOF MS, followed by database searching. Of these spots, 28 were positively identified with known functions in photosynthesis and carbon metabolism (61%), protein destination and storage (11%), as well as those involved in redox/response to stress, followed by proteins involved in protein synthesis and disease/defence (7%), nucleotide and transport (3%). RNA-Seq was used to identify differentially expressed genes in response to the fungal infection over five time points namely Day 0, 2, 4, 8 and 12. cDNA libraries were constructed, sequenced using Illumina HiScan SQTM and MiSeqTM instruments. Nucleotide reads were analysed by aligning it to the apple genome using TopHat spliceaware aligner software, followed by analysis with limma/voom and edgeR, R statistical packages for finding differentially expressed genes. These results showed that 398 genes were differentially expressed in response to fungal infection over the five time points. These mapped to 1164 transcripts in the apple transcripts database, which were submitted to BLAST2GO. Eighty-six percent of the genes obtained a BLAST hit to which 77% of the BLAST hits were assigned GO terms. These were classed into three ontology categories i.e. biological processes, molecular function and cellular components. By focussing on the host responsive genes, modulation of genes involved in signal perception, transcription, stress/detoxification, defence related proteins, transport and secondary metabolites have been observed. A comparative analysis was performed between the Day 4 proteomic and Day 4 transcriptomic data. In the infected and uninfected apple leaf proteome of Day 4, we found 9 proteins responsive to fungal infection were up-regulated. From the transcriptome data of Day 4, 162 genes were extracted, which mapped to 395 transcripts in the apple transcripts. These were submitted to BLAST2GO for functional annotation. Proteins encoded by the up-regulated transcripts were functionally categorised. Pathways affected by the up-regulated genes are carbon metabolism, protein synthesis, defence, redox/response to stress. Up-regulated genes were involved in signal perception, transcription factors, stress/detoxification, defence related proteins, disease resistance proteins, transport and secondary metabolites. We found that the same pathways including energy, disease/defence and redox/response to stress were affected for the comparative analysis. The results of this study can be used as a starting point for targeting host responsive genes in genetic manipulation of apple cultivars.

Amongst the many processes that shape the final set of RNA molecules present in eukaryote cells, splicing emerges as the most prominent mechanism for message diversification. In recent years, applications of high throughput sequencing to RNA, known as RNA sequencing, have opened new avenues for the study of transcriptome composition, and have enabled further characterisation of such mechanism. In this thesis, I focus on the application of this technology to the study of human transcript diversity and its potential impact on the protein repertoire. In the first results chapter, I explore the extent of transcriptome diversity by asking whether there is a preference for the production of specific alternative transcripts within each given gene. I show that while many alternative transcripts can be detected, the expression of most protein coding genes tends to be dominated by one single transcript (major transcript). Such findings are validated in the second chapter, and are further used to explore changes in splicing patterns in a disease context. By analysing healthy and tumor samples from kidney cancer patients, I show that most of the detected splicing alterations do not lead to big changes in the relative abundance of major transcripts, at least in a recurrent manner. In addition, I introduce a framework to visualise the most extreme changes in splicing and to evaluate their potential functional impact. In the third chapter, I investigate the role of spliceosome assembly dynamics on the regulation of splice site choice. I show that depletion of PRPF8, a core spliceosomal component, leads to the preferential retention of a subset of introns with weaker splice sites, and also introduces alterations in the rate of co-transcriptional splicing. Finally, in the last chapter, I explore the validation of changes in alternative transcript abundance at the protein level, through the integration of results derived from RNA sequencing datasets with those obtained from proteomics experiments. Altogether, the findings described in this thesis provide a global picture on the extent of alternative splicing in the diversification of the transcriptome, expand current knowledge on the splicing reaction, and open new possibilities for the integration of transcriptomics and proteomics data.

abstract: The human transcriptional regulatory machine utilizes hundreds of transcription factors which bind to specific genic sites resulting in either activation or repression of targeted genes. Networks comprised of nodes and edges can be constructed to model the relationships of regulators and their targets. Within these biological networks small enriched structural patterns containing at least three nodes can be identified as potential building blocks from which a network is organized. A first iteration computational pipeline was designed to generate a disease specific gene regulatory network for motif detection using established computational tools. The first goal was to identify motifs that can express themselves in a state that results in differential patient survival in one of the 32 different cancer types studied. This study identified issues for detecting strongly correlated motifs that also effect patient survival, yielding preliminary results for possible driving cancer etiology. Second, a comparison was performed for the topology of network motifs across multiple different data types to identify possible divergence from a conserved enrichment pattern in network perturbing diseases. The topology of enriched motifs across all the datasets converged upon a single conserved pattern reported in a previous study which did not appear to diverge dependent upon the type of disease. This report highlights possible methods to improve detection of disease driving motifs that can aid in identifying possible treatment targets in cancer. Finally, networks where only minimally perturbed, suggesting that regulatory programs were run from evolved circuits into a cancer context. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2020

The group of Mycobacteria is one of the most intensively studied bacterial taxa, as they cause the two historical and worldwide known diseases: leprosy and tuberculosis. Mycobacteria not identified as tuberculosis or leprosy complex, have been referred to by ‘environmental mycobacteria’ or ‘Nontuberculous mycobacteria (NTM). Mycobacterium kansasii (M. kansasii) is one of the most frequent NTM pathogens, as it causes pulmonary disease in immuno-competent patients and pulmonary, and disseminated disease in patients with various immuno-deficiencies. There have been five documented subtypes of this bacterium, by different molecular typing methods, showing that type I causes tuberculosis-like disease in healthy individuals, and type II in immune-compromised individuals. The remaining types are said to be environmental, thereby, not causing any diseases. The aim of this project was to conduct a comparative genomic study of M. kansasii types I-V and investigating the gene expression level of those types. From various comparative genomics analysis, provided genomics evidence on why M. kansasii type I is considered pathogenic, by focusing on three key elements that are involved in virulence of Mycobacteria: ESX secretion system, Phospholipase c (plcb) and Mammalian cell entry (Mce) operons. The results showed the lack of the espA operon in types II-V, which renders the ESX- 1 operon dysfunctional, as espA is one of the key factors that control this secretion system. However, gene expression analysis showed this operon to be deleted in types II, III and IV. Furthermore, plcB was found to be truncated in types III and IV. Analysis of Mce operons (1-4) show that mce-1 operon is duplicated, mce-2 is absent and mce-3 and mce-4 is present in one copy in M. kansasii types I-V. Gene expression profiles of type I-IV, showed that the secreted proteins of ESX-1 were slightly upregulated in types II-IV when compared to type I and the secreted forms of ESX-5 were highly down regulated in the same types. Differentially expressed genes in types II-IV were also evaluated and validated by qPCR for selected genes. This study gave a general view of the genome of this bacterium and its types, highlighted some different aspects of its subtypes and supplemented by gene expression data.

Marine ecosystems are already threatened by the effects of climate change through increases in ocean temperatures and pCO2 levels due to increasing atmospheric CO2. Marine fish living close to their thermal maximum have been shown to be especially vulnerable to temperatures exceeding that threshold, and even relatively small increases in elevated pCO2 levels have led to behavioral impairments with amplified predation risks. These ongoing threats highlight the need for further understanding of how these changes will impact fish and if any potential for adaptation or acclimation exists. The coral reef fish, Acanthochromis polyacanthus, has been well studied in response to singular environmental changes both through its phenotype and molecular expression profiles within and across generations. However, key questions regarding transgenerational heritability and molecular responses to multiple environmental changes have not been addressed. To further understand A. polyacanthus I examined the mechanisms behind heritability of behavioral tolerance to elevated pCO2 in an attempt to determine the maternal and paternal contributions to this phenotype. There was a strong impact of parental phenotype on the expression profiles of their offspring regardless of environmental exposure. Offspring from both parental pairs expressed mechanisms involved in tolerance to ocean acidification suggesting this phenotype is reliant on input from both parents. Creation of a new proteomic resource, a SWATH spectral library, delivered a closer examination of the link between phenotypic and expression changes. Analysis on different constructed libraries led to the use of an organism whole library combined with study specific data to analyze proteomic changes in A. polyacanthus under the combined environmental changes of ocean acidification and warming. With direct comparisons to transcriptomic changes in the same individuals I identified an additive effect of elevated pCO2 and temperature associated with decreases in growth and development. However, a strong role of parental identity on the expression profiles of offspring reinforced the high genetic variability of this species. This thesis provides novel insights into the heritability of phenotypic traits and the molecular responses to combined stressors in A. polyacanthus, as well as presenting a new resource for proteomic studies in this fish and other non-model species.

Modern advances in sequencing technologies have enabled exploration of molecular biology at unprecedented scale and resolution. Transcriptome sequencing (RNA-seq), in particular, has been widely adopted as a routine cost-effective method for assaying both genetic and functional characteristics of biological systems with resolution down to individual cells. Clinical research and applications leveraging these technologies have largely targeted tumor biology, where transcriptome sequencing can capture tumor genetic and epigenetic characteristics and aid with understanding the etiology or guide treatments. Specialized computational methods and bioinformatic software tools are essential for processing and analyzing RNA-seq to explore various aspects of tumor biology including driver mutations, genome rearrangements, and aneuploidy. With single cell resolution, such methods can yield insights into tumor cellular composition and heterogeneity. Here, we developed methods and tools to support cancer transcriptome studies for bulk and single cell tumor transcriptomes, focusing primarily on fusion transcript detection and predicting large-scale copy number alternations from RNA-seq. These efforts culminated in the development of STAR-Fusion for fast and accurate detection of fusion transcripts, FusionInspector for further characterizing predicted fusion transcripts and discriminating likely artifacts, and TrinityFusion for de novo reconstruction of fusion transcripts and tumor viruses. We also developed advanced methods for predicting copy number alterations and subclonal architecture from tumor and normal single cell RNA-seq data, as incorporated into our InferCNV software. In addition to these bioinformatic method and software developments, we applied our fusion detection methods to thousands of tumor and normal samples and gain novel insights that should further help guide researchers with clinical applications of fusion transcript discovery.