Computational Integrative Models for Cellular Conversion: Application to Cellular Reprogramming and Disease Modeling

The groundbreaking identification of only four transcription factors that are able to induce pluripotency in any somatic cell upon perturbation stimulated the discovery of copious amounts of instructive factors triggering different cellular conversions. Such conversions are highly significant to regenerative medicine with its ultimate goal of replacing or regenerating damaged and lost cells. Precise directed conversion of damaged cells into healthy cells offers the tantalizing prospect of promoting regeneration in situ. In the advent of high-throughput sequencing technologies, the distinct transcriptional and accessible chromatin landscapes of several cell types have been characterized. This characterization provided clear evidences for the existence of cell type specific gene regulatory networks determined by their distinct epigenetic landscapes that control cellular phenotypes. Further, these networks are known to dynamically change during the ectopic expression of genes initiating cellular conversions and stabilize again to represent the desired phenotype. Over the years, several computational approaches have been developed to leverage the large amounts of high-throughput datasets for a systematic prediction of instructive factors that can potentially induce desired cellular conversions. To date, the most promising approaches rely on the reconstruction of gene regulatory networks for a panel of well-studied cell types relying predominantly on transcriptional data alone. Though useful, these methods are not designed for newly identified cell types as their frameworks are restricted only to the panel of cell types originally incorporated. More importantly, these approaches rely majorly on gene expression data and cannot account for the cell type specific regulations modulated by the interplay of the transcriptional and epigenetic landscape. In this thesis, a computational method for reconstructing cell type specific gene regulatory networks is proposed that aims at addressing the aforementioned limitations of current approaches. This method integrates transcriptomics, chromatin accessibility assays and available prior knowledge about gene regulatory interactions for predicting instructive factors that can potentially induce desired cellular conversions. Its application to the prioritization of drugs for reverting pathologic phenotypes and the identification of instructive factors for inducing the cellular conversion of adipocytes into osteoblasts underlines the potential to assist in the discovery of novel therapeutic interventions

Computational Integrative Models for Cellular Conversion: Application to Cellular Reprogramming and Disease Modeling

The groundbreaking identification of only four transcription factors that are able to induce pluripotency in any somatic cell upon perturbation stimulated the discovery of copious amounts of instructive factors triggering different cellular conversions. Such conversions are highly significant to regenerative medicine with its ultimate goal of replacing or regenerating damaged and lost cells. Precise directed conversion of damaged cells into healthy cells offers the tantalizing prospect of promoting regeneration in situ. In the advent of high-throughput sequencing technologies, the distinct transcriptional and accessible chromatin landscapes of several cell types have been characterized. This characterization provided clear evidences for the existence of cell type specific gene regulatory networks determined by their distinct epigenetic landscapes that control cellular phenotypes. Further, these networks are known to dynamically change during the ectopic expression of genes initiating cellular conversions and stabilize again to represent the desired phenotype. Over the years, several computational approaches have been developed to leverage the large amounts of high-throughput datasets for a systematic prediction of instructive factors that can potentially induce desired cellular conversions. To date, the most promising approaches rely on the reconstruction of gene regulatory networks for a panel of well-studied cell types relying predominantly on transcriptional data alone. Though useful, these methods are not designed for newly identified cell types as their frameworks are restricted only to the panel of cell types originally incorporated. More importantly, these approaches rely majorly on gene expression data and cannot account for the cell type specific regulations modulated by the interplay of the transcriptional and epigenetic landscape. In this thesis, a computational method for reconstructing cell type specific gene regulatory networks is proposed that aims at addressing the aforementioned limitations of current approaches. This method integrates transcriptomics, chromatin accessibility assays and available prior knowledge about gene regulatory interactions for predicting instructive factors that can potentially induce desired cellular conversions. Its application to the prioritization of drugs for reverting pathologic phenotypes and the identification of instructive factors for inducing the cellular conversion of adipocytes into osteoblasts underlines the potential to assist in the discovery of novel therapeutic interventions

Regulation of self-renewal and detection of karyotypic changes of pluripotent human embryonic stem cells

Human embryonic stem cells are pluripotent cells capable of renewing themselves and differentiating to specialized cell types. Because of their unique regenerative potential, pluripotent cells offer new opportunities for disease modeling, development of regenerative therapies, and treating diseases. Before pluripotent cells can be used in any therapeutic applications, there are numerous challenges to overcome. For instance, the key regulators of pluripotency need to be clarified. In addition, long term culture of pluripotent cells is associated with the accumulation of karyotypic abnormalities, which is a concern regarding the safe use of the cells for therapeutic purposes. The goal of the work presented in this thesis was to identify new factors involved in the maintenance of pluripotency, and to further characterize molecular mechanisms of selected candidate genes. Furthermore, we aimed to set up a new method for analyzing genomic integrity of pluripotent cells. The experimental design applied in this study involved a wide range of molecular biology, genome-wide, and computational techniques to study the pluripotency of stem cells and the functions of the target genes. In collaboration with instrument and reagent company Perkin Elmer, KaryoliteTM BoBsTM was implemented for detecting karyotypic changes of pluripotent cells. Novel genes were identified that are highly and specifically expressed in hES cells. Of these genes, L1TD1 and POLR3G were chosen for further investigation. The results revealed that both of these factors are vital for the maintenance of pluripotency and self-renewal of the hESCs. KaryoliteTM BoBsTM was validated as a novel method to detect karyotypic abnormalities in pluripotent stem cells. The results presented in this thesis offer significant new information on the regulatory networks associated with pluripotency. The results will facilitate in understanding developmental and cancer biology, as well as creating stem cell based applications. KaryoliteTM BoBsTM provides rapid, high-throughput, and cost-efficient tool for screening of human pluripotent cell cultures.Siirretty Doriast

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field

Reprograming Neuronal Cells by Overexpression of Fibroblast-Specific Transcription Factors

In mammals, a complex system of regulatory signals distinguishes tissues, structures and functions. Combinations of transcription factors and co-factors regulate activation and repression of genes that result in cellular differentiation. Whole genome arrays allow the monitoring of genomic expression in specific tissues. Fibroblast microarray studies have shown candidate genes that may be involved in fibroblast identification, including genes that express transcription factors Prrx1, Snai2 and Twist1. A previous study showed that the Prrx1 and Snai2 could reactivate a fibroblast phenotype in hybrid cells that had lost fibroblast identity. Furthermore, overexpression of these factors in liver-derived cells strongly repressed liver gene expression and activated fibroblast expression. Based on these observations, expression plasmids containing Prrx1, Snai2 and Twist1, expression cassettes were transfected independently into mouse Neuro2A neuronal cells using standard transfection technique, followed by the selection of G418-resistant clones (pool and clones). Expression of essential fibroblast marker genes and neuronal genes was monitored in transfected cells and non-transfected cells using qualitative real-time polymerase chain reaction (RT-qPCR) on cDNA derived for isolated RNA. Results showed that, surprisingly, little activation of expression occurred for any of the fibroblast genes tested. Rather, strong repression of several fibroblast genes was observed. However, both Snai2 and Prrx1 did appear to strongly repress several neural genes tested, suggesting a partial reprograming of the Neuro2A cells away from a neural phenotype

Decoding co-/post-transcriptional complexities of plant transcriptomes and epitranscriptome using next-generation sequencing technologies

Next-generation sequencing (NGS) technologies – Illumina RNA-seq, Pacific Biosciences isoform sequencing (PacBio Iso-seq), and Oxford Nanopore direct RNA sequencing (DRS) - have revealed the complexity of plant transcriptomes and their regulation at the co-/posttranscriptional level. Global analysis of mature mRNAs, transcripts from nuclear run-on assays, and nascent chromatin-bound mRNAs using short as well as full-length and single-molecule DRS reads have uncovered potential roles of different forms of RNA polymerase II during the transcription process, and the extent of co-transcriptional pre-mRNA splicing and polyadenylation. These tools have also allowed mapping of transcriptome-wide start sites in cap-containing RNAs, poly(A) site choice, poly(A) tail length, and RNA base modifications. Analysis of a large number of plant transcriptomes using high-throughput short and long reads under different conditions has established that diverse abiotic and biotic stresses and environmental cues such as light, which regulates many aspects of plant growth and development, have a profound impact on gene expression at the co-/post-transcriptional level. The emerging theme from these studies is that reprogramming of gene expression in response to developmental cues and stresses at the co-/post transcriptional level likely plays a crucial role in eliciting appropriate responses for optimal growth and plant survival under adverse conditions. Although the mechanisms by which developmental cues and different stresses regulate co-/posttranscriptional splicing are largely unknown, a few recent studies are beginning to provide some insights into these mechanisms. These studies indicate that the external cues target spliceosomal and splicing regulatory proteins to modulate alternative splicing. In this review, we provide an overview of recent discoveries on the dynamics and complexities of plant transcriptomes, mechanistic insights into splicing regulation, and discuss critical gaps in co-/post-transcriptional research that need to be addressed using diverse genomic and biochemical approaches