Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Deciphering transcriptional regulation in cancer cells and development of a new method to identify key transcriptional regulators and their target genes

Cancer cells accumulate genetic changes during carcinogenesis. The dimension of these changes range from point mutations to large chromosomal aberrations. It has been widely accepted that essential genetic programs are thereby dysregulated that normally would prevent uncontrolled cellular division and growth. Transcription factors (TFs) are key proteins of gene regulation and are frequently associated with genetic pathologies, e.g. MYCN in neuroblastomas (NBs). Research on gene regulation -in general or condition-specific- thus is a central aspect in cancer research, and it is also the focus of my work. In a carcinogenesis model of NBs without MYCN-amplification, mutations of chromosome 11q (11q-CNA) are suspected to critically influence tumor development. We were able to refine this model by means of gene expression analysis on 11q-CNA in NBs with different clinical outcome. Gene expression profiles of NBs with unfavorable progression differed significantly between tumors with and without 11q-CNA, whereas 11q-CNA in NBs with favorable outcome is apparently compensated by a yet unknown mechanism. The TF-encoding gene CAMTA1 is located on the chromosomal region 1p, which is frequently deleted in NBs. In vitro experiments with ectopic induction of CAMTA1 yielded CAMTA1-regulated genes with different gene expression profiles that were functionally associated by enrichment analyses with cell cycle regulation and neuronal differentiation. The suggested role of CAMTA1 as a tumor suppressor gene was confirmed by additional in vivo experiments. Furthermore, we studied the effect of MYC and MYCN in NBs without MYCN-amplification and found that these TF also strongly regulate a large number of common target genes according to their own gene expression in these tumors. Promoter analyses and chromatin immunoprecipitation additionally supported the regulation of the determined target genes by MYC/MYCN. The genome-wide application of promoter and enrichment analyses on gene expression data from mouse models enabled us to predict target TFs of Rage signaling. E2f1 and E2f4 were validated experimentally as components of the Rage-dependent gene regulatory network. Finally, we used our experience from gene expression analysis to develop a novel machine learning method to precisely predict TF target gene relationships in human. We combined results from a genome-wide correlation meta-analysis on 4064 microarray gene expression profiles and promoter analyses on TF binding sites with known regulatory interactions between TFs and target genes in our approach. Our method outperformed other comparable methods in human, as we improved shortcomings of other algorithms specifically for higher eukaryotes, in particular the frequently (erroneously) assumed correlation between the mRNA expression of TFs and their target genes. We made our method freely available as a software package with multiple applications like the identification of key TFs in a multiplicity of cellular systems (e.g. cancer cells)

Time-delayed models of gene regulatory networks

We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternativemodelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems

An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis.

BACKGROUND: DosR is an important regulator of the response to stress such as limited oxygen availability in Mycobacterium tuberculosis. Time course gene expression data enable us to dissect this response on the gene regulatory level. The mRNA expression profile of a regulator, however, is not necessarily a direct reflection of its activity. Knowing the transcription factor activity (TFA) can be exploited to predict novel target genes regulated by the same transcription factor. Various approaches have been proposed to reconstruct TFAs from gene expression data. Most of them capture only a first-order approximation to the complex transcriptional processes by assuming linear gene responses and linear dynamics in TFA, or ignore the temporal information in data from such systems. RESULTS: In this paper, we approach the problem of inferring dynamic hidden TFAs using Gaussian processes (GP). We are able to model dynamic TFAs and to account for both linear and nonlinear gene responses. To test the validity of the proposed approach, we reconstruct the hidden TFA of p53, a tumour suppressor activated by DNA damage, using published time course gene expression data. Our reconstructed TFA is closer to the experimentally determined profile of p53 concentration than that from the original study. We then apply the model to time course gene expression data obtained from chemostat cultures of M. tuberculosis under reduced oxygen availability. After estimation of the TFA of DosR based on a number of known target genes using the GP model, we predict novel DosR-regulated genes: the parameters of the model are interpreted as relevance parameters indicating an existing functional relationship between TFA and gene expression. We further improve the prediction by integrating promoter sequence information in a logistic regression model. Apart from the documented DosR-regulated genes, our prediction yields ten novel genes under direct control of DosR. CONCLUSIONS: Chemostat cultures are an ideal experimental system for controlling noise and variability when monitoring the response of bacterial organisms such as M. tuberculosis to finely controlled changes in culture conditions and available metabolites. Nonlinear hidden TFA dynamics of regulators can be reconstructed remarkably well with Gaussian processes from such data. Moreover, estimated parameters of the GP can be used to assess whether a gene is controlled by the reconstructed TFA or not. It is straightforward to combine these parameters with further information, such as the presence of binding motifs, to increase prediction accuracy.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

Advances in quantitative microscopy

Microscopy allows us to peer into the complex deeply shrouded world that the cells of our body grow and thrive in. With the emergence of automated digital microscopes and software for anlysing and processing the large numbers of image that they produce; quantitative microscopy approaches are now allowing us to answer ever larger and more complex biological questions. In this thesis I explore two trends. Firstly, that of using quantitative microscopy for performing unbiased screens, the advances made here include developing strategies to handle imaging data captured from physiological models, and unsupervised analysis screening data to derive unbiased biological insights. Secondly, I develop software for analysing live cell imaging data, that can now be captured at greater rates than ever before and use this to help answer key questions covering the biology of how cells make the decision to arrest or proliferate in response to DNA damage. Together this thesis represents a view of the current state of the art in high-throughput quantitative microscopy and details where the field is heading as machine learning approaches become ever more sophisticated.Open Acces

The role of FOXM1 and its cofactors HMGA1 and HMGN1 in breast cancer progression

FOXM1 is a potent oncogenic transcription factor that plays essential roles in multiple hallmarks of cancer progression, thus having impacts on tumour initiation, invasion, angiogenesis, cancer stemness and drug sensitivity. Likewise, high mobility group proteins, HMGA1 and HMGN1 are also involved in tumorigenic and metastatic processes in cancer. The human protein-protein interactions prediction database has reported the potential interactions between these proteins, but no further experimental evidence to confirm and characterise this link. In my project, I demonstrated that FOXM1 binds to HMGA1 or HMGN1 as protein-protein complex, and such interactions affect the transcriptional regulation of KIF20A and SQSTM1/p62. Disruption of the complex or their protein expression levels significantly affects drug sensitivity, cell migration and invasion in breast cancer cells. Apart from the essential roles of HMGA1 and HMGN1 in DNA repair mechanisms, they influence these malignant processes may partially through regulating FOXM1 activities. Thus, the functions of these FOXM1’s transcriptional targets, KIF20A and p62 were further examined. The results showed the p62 is involved in modulating angiogenic and metastatic processes in breast cancer as silencing of p62 significantly attenuated tumour-induced angiogenesis and cell migration in zebrafish. Furthermore, KIF20A is a novel target which KIF20A inhibition increased drug sensitivity and reduced cell migration, invasion and mammosphere formation. In addition, the new KIF20A inhibitor, BKS0349 compound offers a viable therapeutic option for cancer treatment in future. In summary, my results have established a potential mechanism underlying the interactions between FOXM1, HMGA1 and HMGN1 in breast cancer cells. Collectively, these findings strengthen our current understanding of the oncogenic protein FOXM1-mediated cancer progression and provide new insights into cancer diagnosis and treatment.Open Acces

Nonsense-mediated mRNA decay in human cells: mechanistic insights, functions beyond quality control and the double-life of NMD factors

Nonsense-mediated decay is well known by the lucid definition of being a RNA surveillance mechanism that ensures the speedy degradation of mRNAs containing premature translation termination codons. However, as we review here, NMD is far from being a simple quality control mechanism; it also regulates the stability of many wild-type transcripts. We summarise the abundance of research that has characterised each of the NMD factors and present a unified model for the recognition of NMD substrates. The contentious issue of how and where NMD occurs is also discussed, particularly with regard to P-bodies and SMG6-driven endonucleolytic degradation. In recent years, the discovery of additional functions played by several of the NMD factors has further complicated the picture. Therefore, we also review the reported roles of UPF1, SMG1 and SMG6 in other cellular processe

A systems approach to clinical oncology: Focus on breast cancer

During the past decade, genomic microarrays have been applied with some success to the molecular profiling of breast tumours, which has resulted in a much more detailed classification scheme as well as in the identification of potential gene signature sets. These gene sets have been applied to both the prognosis and prediction of outcome to treatment and have performed better than the current clinical criteria. One of the main limitations of microarray analysis, however, is that frozen tumour samples are required for the assay. This imposes severe limitations on access to samples and precludes large scale validation studies from being conducted. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), on the other hand, can be used with degraded RNAs derived from formalin-fixed paraffin-embedded (FFPE) tumour samples, the most important and abundant source of clinical material available. More recently, the novel DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay has been developed as a high throughput gene expression profiling system specifically designed for use with FFPE tumour tissue samples. However, we do not believe that genomics is adequate as a sole prognostic and predictive platform in breast cancer. The key proteins driving oncogenesis, for example, can undergo post-translational modifications; moreover, if we are ever to move individualization of therapy into the practical world of blood-based assays, serum proteomics becomes critical. Proteomic platforms, including tissue micro-arrays (TMA) and protein chip arrays, in conjunction with surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF/MS), have been the technologies most widely applied to the characterization of tumours and serum from breast cancer patients, with still limited but encouraging results. This review will focus on these genomic and proteomic platforms, with an emphasis placed on the utilization of FFPE tumour tissue samples and serum, as they have been applied to the study of breast cancer for the discovery of gene signatures and biomarkers for the early diagnosis, prognosis and prediction of treatment outcome. The ultimate goal is to be able to apply a systems biology approach to the information gleaned from the combination of these techniques in order to select the best treatment strategy, monitor its effectiveness and make changes as rapidly as possible where needed to achieve the optimal therapeutic results for the patient

MicroRNA-34a: A Key Regulator in the Hallmarks of Renal Cell Carcinoma

Renal cell carcinoma (RCC) incidence has increased over the past two decades. Recent studies reported microRNAs as promising biomarkers for early cancer detection, accurate prognosis, and molecular targets for future treatment. This study aimed to evaluate the expression levels of miR-34a and 11 of its bioinformatically selected target genes and proteins to test their potential dysregulation in RCC. Quantitative real-time PCR for miR-34a and its targets; MET oncogene; gene-regulating apoptosis (TP53INP2 and DFFA); cell proliferation (E2F3); and cell differentiation (SOX2 and TGFB3) as well as immunohistochemical assay for VEGFA, TP53, Bcl2, TGFB1, and Ki67 protein expression have been performed in 85 FFPE RCC tumor specimens. Clinicopathological parameter correlation and in silico network analysis have also implicated. We found RCC tissues displayed significantly higher miR-34a expression level than their corresponding noncancerous tissues, particularly in chromophobic subtype. MET and E2F3 were significantly upregulated, while TP53INP2 and SOX2 were downregulated. ROC analysis showed high diagnostic performance of miR-34a (AUC = 0.854), MET (AUC = 0.765), and E2F3 (AUC = 0.761). The advanced pathological grade was associated with strong TGFB1, VEGFA, and Ki67 protein expression and absent Tp53 staining. These findings indicate miR-34a along with its putative target genes could play a role in RCC tumorigenesis and progression