EGIA–evolutionary optimisation of gene regulatory networks, an integrative approach

Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored, to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement

Data integration for microarrays: enhanced inference for gene regulatory networks

Microarray technologies have been the basis of numerous important findings regarding gene expression in the last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related e.g. to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple data sets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come

Gene regulatory network modelling with evolutionary algorithms -an integrative approach

Building models for gene regulation has been an important aim of Systems Biology over the past years, driven by the large amount of gene expression data that has become available. Models represent regulatory interactions between genes and transcription factors and can provide better understanding of biological processes, and means of simulating both natural and perturbed systems (e.g. those associated with disease). Gene regulatory network (GRN) quantitative modelling is still limited, however, due to data issues such as noise and restricted length of time series, typically used for GRN reverse engineering. These issues create an under-determination problem, with many models possibly fitting the data. However, large amounts of other types of biological data and knowledge are available, such as cross-platform measurements, knockout experiments, annotations, binding site affinities for transcription factors and so on. It has been postulated that integration of these can improve model quality obtained, by facilitating further filtering of possible models. However, integration is not straightforward, as the different types of data can provide contradictory information, and are intrinsically noisy, hence large scale integration has not been fully explored, to date. Here, we present an integrative parallel framework for GRN modelling, which employs evolutionary computation and different types of data to enhance model inference. Integration is performed at different levels. (i) An analysis of cross-platform integration of time series microarray data, discussing the effects on the resulting models and exploring crossplatform normalisation techniques, is presented. This shows that time-course data integration is possible, and results in models more robust to noise and parameter perturbation, as well as reduced noise over-fitting. (ii) Other types of measurements and knowledge, such as knock-out experiments, annotated transcription factors, binding site affinities and promoter sequences are integrated within the evolutionary framework to obtain more plausible GRN models. This is performed by customising initialisation, mutation and evaluation of candidate model solutions. The different data types are investigated and both qualitative and quantitative improvements are obtained. Results suggest that caution is needed in order to obtain improved models from combined data, and the case study presented here provides an example of how this can be achieved. Furthermore, (iii), RNA-seq data is studied in comparison to microarray experiments, to identify overlapping features and possibilities of integration within the framework. The extension of the framework to this data type is straightforward and qualitative improvements are obtained when combining predicted interactions from single-channel and RNA-seq datasets

Modulation of the Mitogen Activated Protein Kinase Pathway Spatiotemporal Signalling Components: Influence on Pathway Activation Behaviour Using an Agent Based Model

The subtleties of how the Mitogen Activated Protein Kinase works (MAPK) biochemical signalling pathway works, its emergent oscillatory behaviour and sensitivity is explained though an analysis of a computational agent based model that takes into account the distribution of the MAPK cascade components into multiple compartment

Accumulation of Somatic Mutations in Normal Human Colonic Epithelium

I investigated stem cell dynamics in normal human colon by detecting somatic variants affecting X-linked and autosomal genes using immunohistochemistry. Applying neutral clonal marks to a large cohort of patients to interpret age-related trends in clone frequencies has established the baseline stem cell dynamics of the tissue. Analysis of a number of new clonal marks showing biased behaviours suggests that different gene-specific mutations can subvert constraints resulting from the tissue architecture in different ways. In respect of intra-gland competition of stem cell derived clones, a disadvantage is observed for the histone modifier HDAC6, while at the other end of the spectrum, loss of the cohesin member STAG2 strongly advantages affected stem cells. Subsequent clonal expansion beyond the boundary of a single crypt is recognised by clones occupying multi-crypt patches. Quantification of such events allows the rate of lateral expansion for different mutations to be measured. Moderate effects were found for PTEN, p53 and STAG2, while mutations in the histone demethylase KDM6A generate very large areas of mutant epithelium in aged humans. Further, targeted sequencing revealed dramatic expansion of KRAS-mutant clones in histologically normal colon. Patches may arise from crypt fission and fusion events. Using a clonal mark based on mild periodic acid-Schiff staining, the neutral crypt fission and fusion rates were quantified. Against this baseline, it was found that KDM6A-mutant clones expand mainly by fission, while fusion remains at homeostatic levels. The emerging picture is that of the aged human colon consisting of a mosaic of different mutations. The work presented in this thesis offers detailed insights into the rates at which different gene-specific mutations arising in colonic stem cells can become fixed within individual crypts and undergo subsequent lateral expansion through crypt fission and fusion events. This defines the timeframe taken for cancer drivers to achieve high mutant allele burden within the tissue, which can serve as a basis for cancer prevention strategies.Wellcome Trus

Identification of genetic interactions in a S. pombe yeast model for juvenile CLN3 disease

Juvenile CLN3 disease is a rare lysosomal storage disease, and the most common cause of neurodegeneration in children. Since the identification of the disease gene (CLN3) in 1995, efforts in various cellular and animal models have led to CLN3 being associated with several cellular pathologies. However, its precise molecular function remains unknown. Furthermore, with only a limited knowledge of disease pathogenesis, there remains a considerable need to identify therapeutic targets in order to accelerate the development of therapies for this prematurely fatal disease. Schizosaccharomyces pombe has proved to be a powerful and accurate model to help elucidate the function of CLN3 by virtue of its evolutionary conserved orthologue, btn1+. To date it has revealed both a new cellular localisation and potential functions for btn1+. The primary aim of this project was therefore to further exploit this fission yeast (btn1∆) model, and use synthetic genetic array analysis (SGAs) to identify genome- wide, genetic interactions of btn1+. It was hypothesised that defining the genetic relationships of btn1+ would provide insight into its molecular role, which in turn could be used to infer CLN3 gene function. It was also hoped that such information could be used to identify therapeutic targets and help refine the focus of future work in higher eukaryotic models. Using such an approach, this thesis provides a considerable step in our understand- ing of the function of btn1+, and by extrapolation CLN3, by suggesting that their function likely converges with various stress response pathways. A comprehensive characterization of the btn1∆ strain revealed numerous phenotypes co-incident with known stress response mutants, suggesting that this strain indeed displays compromised stress signalling which may contribute to disease pathogenesis. This thesis also details a number of therapeutic targets as the genetic manipulation of two highly conserved mitogen activated protein kinase pathways (the cell wall integrity (CWI) pathway and stress associated protein kinase (SAPK) pathway) and the interconnected TOR kinase complexes, are shown to rescue aspects, if not all, of the btn1∆ phenotypes investigated. This is the most successful rescue of disease phenotypes in any model for juvenile CLN3 disease to date

Utilising Yeast as a Model Organism to Deconstruct the Regulation of Tumour Associated Lipogenesis

It is important for cells to respond to external signals. Central to these responses are the sensing and signalling pathways that communicate with the nucleus and facilitate necessary changes in gene expression. Of particular importance are the mitogen-activated protein kinase (MAPK) and the mammalian target of rapamycin (mTOR) pathways. Both of these pathways have been shown to be involved in cell growth, proliferation, motility and survival. They are under intensive investigation in connection with cancer with recent evidence suggests their role in mediating lipogenesis. Lipogenesis accompanies a variety of disease states, including the formation of brain tumours. Malignant brain tumours are rapidly growing and often invade surrounding healthy tissue, resulting in poor prognosis for the patient. The ability to limit tumour growth and reduce invasion through a better understanding of tumour associated lipid formation may offer targets for the development of new therapies. Yeast is frequently used as a paradimic organism for the study of human diseases. In this study a Nile red assay has been developed, optimised and validated to measure levels of both polar and neutral lipids within yeast cells. This method has been utilised in the yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe, to study the role of the MAPK pathways in regulating lipid accumulation. Data in this thesis demonstrates that stress-activated protein kinase pathways (SAPK) play a key role in regulating lipid accumulation upon nitrogen limitation, as cells enter the stationary phase of growth. Evidence from S. cerevisiae proposes that the lipogenic switch occurs in two phases, with the central MAPK (Hog1) activated in both a MAPKK (Pbs2) independent and dependent manner. Analysis of Hog1 phosphorylation during various growth phases, suggests that there are previously uncharacterised sites on Hog1 which are potentially phosphorylated during phase one by the protein kinase Sch9, a target of the Tor1 complex. The second phase results in Hog1 being dually phosphorylated by the canonical pathway, via Pbs2p. It is proposed that Hog1 may have a number of downstream cytoplasmic and nuclear targets, including lipid related enzymes (Dga1) and transcription factors (Msn2/4). Data also suggests that lipid accumulation in S. pombe is also regulated in a similar manner. The oleaginous yeast Lipomyces starkeyi is able to accumulate high levels of lipid and has similarity to lipid enzymes found in mammalian cells. As such, it was proposed that L. starkeyi may be utilised as a model organism to further characterise the role of MAPK in lipid accumulation. Information from stress response studies and bioinformatics suggests the MAPK pathway in L. starkeyi is highly conserved. However, the application of yeast molecular tools to L. starkeyi was unsuccessful, demonstrating that further work is required to develop its use as a model organism. Data in this thesis has shown a novel role for the SAPK pathways in regulating lipid accumulation in yeast. It has also demonstrated cross talk between the MAPK and TOR pathways, resulting in an integrated cellular response. The high level of conservation of these pathways across species, suggests that directly targeting these pathways in cancer cells may reduce tumour associated lipogenesis, therefore inhibiting growth of glioma. With current treatments only delivering limited results, this could help extend patient survival

The role of PTEN as a PI(3,4)P2 lipid phosphatase in Class I phosphoinositide 3-kinase signalling

Name: Anna Jadwiga Kielkowska Dissertation title: The role of PTEN as a PI(3,4)P2 lipid phosphatase in Class I phosphoinositide 3-kinase signalling Abstract Class I phosphoinositide 3-kinases (Class I PI3Ks) are essential players involved in the signalling events in the cell and are critical promoters of cellular growth, survival and metabolism. Once activated by environmental stimuli such as growth factors, cytokines or antigens, they exert their catalytic activity by phosphorylating phosphatidylinositol (4,5)-bisphosphate (PI(4,5)P2) to yield a second messenger - PI(3,4,5)P3. Unrestrained PI(3,4,5)P3 signalling has been classically associated with hyperactivation of the Class I PI3K/AKT pathway and has been shown to be a molecular trigger of many pathophysiologies in humans, including autoimmune disorders, respiratory diseases and cancer. To date, two classes of lipid phosphatases SHIP1/2 and PTEN have been reported, which dephosphorylate PI(3,4,5)P3 on positions 5’ and 3’ of the inositol ring to generate PI(3,4)P2 and PI(4,5)P2 respectively, and thus quench Class I PI3K signalling. Moreover, PI(3,4)P2 levels in the cell are regulated by two important lipid 4-phosphatases - INPP4A/B. While the role of PTEN as a tumour suppressor is well established, functions of SHIP1/2 and INPP4A/B are just starting to emerge. A major barrier to progress in this field has been the lack of high quality measurements of PI(3,4)P2, to assess the impact it may have on shaping cellular behaviour. This dissertation summarises the work performed to develop a novel, HPLC-ESI MS/MS based method, in order to measure the product of PI(3,4,5)P3 5-dephosphorylation, PI(3,4)P2, separated from its more abundant regioisomer in cells - PI(4,5)P2. This and an existing HPLC-ESI MS/MS method for measuring PI(3,4,5)P3, have enabled us to describe the fluxes through Class I PI3K-controlled PI(3,4,5)P3 generation and its subsequent 3- and 5- dephosphorylation pathways in human mammary epithelial cells (Mcf10a) stimulated with epidermal growth factor (EGF). By means of genetic suppression of PTEN and INPP4B, we revealed an unexpectedly high level of PI(3,4)P2 that accumulates in EGF-stimulated PTEN-INPP4B-KO Mcf10a cells. Further, an in vitro biochemical assay suggested a novel role for PTEN as a direct PI(3,4)P2 3-phosphatase in Mcf10a cells. This important observation was supported by in sillico phosphatidylinositol lipid modelling of the relevant pathways. In an effort to understand its potential physiological significance, we demonstrated that PI(3,4)P2 accumulation correlates with the ability of genetically modified Mcf10a cells to form gelatin-degrading invadopodia. Finally, we used a mouse prostate cancer model to show PTEN’s importance in controlling PI(3,4)P2 levels in vivo, pointing to a potential role for PI(3,4)P2 in PTEN-dependent tumourigenesis. I hope that the work described in this dissertation will contribute to the current knowledge of phosphatidylinositol lipid biology in the context of Class I PI3K signalling and will simulate future efforts to gain an in-depth understanding of the roles of PTEN and PI(3,4)P2 in cellular physiology

Novel mechanisms of resistance to EGFR inhibitory drugs in non-small cell lung cancer

EGFR activating mutations are present in 10-40% of non-small cell lung cancer. Such mutations render tumour cells sensitive to EGFR tyrosine kinase inhibitors (EGFR TKIs), with responses of up to 80% in populations selected for the presence of an activating mutation. Unfortunately, almost all patients develop resistance after about a year. Clinically described mechanisms of resistance include the presence of a secondary mutation (T790M) in EGFR which prevents EGFR TKIs binding to the EGF receptor, and amplification MET which permits survival signalling via the ERBB3 receptor. However in 30% of cases, the mechanism of acquired resistance to EGFR TKIs is still unknown. My aim was to carry out a genome-wide siRNA screen to identify novel mechanisms of resistance to EGFR TKIs. I identified two genes that have not been implicated in EGFR TKI resistance previously, NF1 and DEPTOR, which are negative regulators of RAS and mTOR respectively. Depletion of NF1 or DEPTOR leads to increased resistance to EGFR TKIs via upregulation of MAPK signalling by direct and indirect mechanisms

Investigating the functional significance of an FGFR2 intronic SNP in Breast Cancer

PhDSingle nucleotide polymorphisms present in the second intron of the fibroblast growth factor receptor 2 (FGFR2) gene have been linked with increased risk of breast cancer in several genome wide association studies. The potential effect of those SNPs appeared to be mediated through the differential binding of cis-regulatory elements, such as transcription factors, since all the SNPs in linkage disequilibrium were located in a regulatory DNA region. Preliminary studies have shown that a Runx2 binding site is functional only in the minor, disease associated allele of rs2981578, resulting in increased expression of FGFR2 in cancers from patients homozygous for that allele. Moreover, the increased risk conferred by the minor FGFR2 allele is associated most strongly in oestrogen receptor alpha positive (ERα) breast tumours, suggesting a potential interaction between ERα and FGFR signalling. Here, we have developed a human cell line model system to study the effect of those SNPs on cell behaviour. In an ERα positive breast cancer cell line, rs2981578 was edited using Zinc Finger Nucleases. Unexpectedly, the acquisition of the single risk allele in MCF7 cells failed to affect proliferation or cell cycle progression. Binding of Runx2 to the risk allele was not observed. However FOXA1 binding, an important ERα partner, appeared decreased at the rs2981578 locus in the risk allele cells. Additionally, differences in allele specific expression (ASE) of FGFR2 were not observed in a panel of 72 ERα positive breast cancer samples. Thus, the apparent increased risk of developing ERα positive breast cancer is not caused by rs2981578 alone. Rather, the observed increased risk of developing breast cancer might be the result of a coordinated effect of multiple SNPs forming a risk haplotype in the second intron of FGFR2