Gambardella, Gennaro Identification of transcriptional and post-translational regulatory networks from gene expression profile: an information-theoretic approach. [Tesi di dottorato]


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Item Type: Tesi di dottorato
Resource language: English
Title: Identification of transcriptional and post-translational regulatory networks from gene expression profile: an information-theoretic approach.
Gambardella, Gennarogambardella@tigem.iy
Institution: Università degli Studi di Napoli Federico II
Department: Biologia e patologia cellullare e molecolare "L. Califano"
Scuola di dottorato: Biotecnologie
Dottorato: Biologia computazionale e bioinformatica
Ciclo di dottorato: 25
Coordinatore del Corso di dottorato:
Di Bernardo,
Keywords: tissue specific regulatory networks; post-translational modification; systems biology;
Settori scientifico-disciplinari del MIUR: Area 05 - Scienze biologiche > BIO/18 - Genetica
Area 13 - Scienze economiche e statistiche > SECS-S/02 - Statistica per la ricerca sperimentale e tecnologica
Date Deposited: 03 Apr 2013 10:17
Last Modified: 16 Jul 2014 12:43

Collection description

The reconstruction of the regulatory interactions among DNA, RNAs and proteins in a cell is probably the most important and key challenge in molecular biology. In the last decade, the introductions of new high-throughput technologies, such as microarrays and, more recently, next generation sequencing (NGS) have facilitated this task. Different Systems Biology approaches have been proposed to reconstruct the transcriptional, post-transcriptional and the post-translational regulatory networks of a cell starting from genomics data. The two aims of the research here described are: (1) the development and the application of a computational method for the identification of tissue-specific, or more broadly, condition-specific pathways; (2) the development and the application of a computational approach for the identification of post-translational modulators of transcription factor activity from gene expression profiles. In Chapter 1, I provide a brief overview of the different molecular networks known to exist in a living cell. Chapter 2 illustrates a comparative study of the different approaches to reverse-engineering gene networks from gene expression profiles (GEPs) and their limitations. Current state-of-the-art reverse-engineering approaches model gene networks as static processes, i.e. regulatory interactions among genes in the network (such as direct physical interactions or indirect functional interactions) do not change across different conditions or tissue types. However, different cell-types, or the same cell-type but in different conditions, may carry out very different functions, thus it is expected that their regulatory networks may reflect these differences. In Chapter 3 and 4, I describe the development of a novel approach named DINA (Differential Network Analysis) for the identification of differentially co-regulated pathways. DINA is based on the hypothesis that genes belonging to a condition-specific pathway are actively co-regulated only when the pathway is active, independently of their absolute level of expression. I first reverse-engineered 30 tissue-specific networks from a collection of about 3000 GEPs. I then applied DINA to these networks in order to identify tissue-specific pathways starting from a list of 110 KEGG-annotated pathways. As expected, DINA predicted many metabolic pathways to be tissue-specific and prevalently active in liver and kidney. I then built a simplified model of hepatocellular carcinoma (HCC) to mimic the HCC progression using three condition-specific regulatory networks obtained from three different cell-lines: (i) primary hepatocyte, (ii) HepG2 and (iii) Huh7. Using these three cell-type specific networks, I demonstrated that DINA can be used to make hypotheses on dysregulated pathways during disease progression. DINA is also able to predict which Transcription Factors (TFs) may be responsible for the pathway condition-specific co-regulation. I tested this approach to identify regulators of tissue-specific metabolic pathways, and I correctly identified Nuclear Receptors as their main regulators. With this method, I was also able to identify a new putative tissue-specific negative regulator of hepatocyte metabolism: Yeats2. In Chapter 5, 6 and 7, I propose a generalized method that I called Differential Multi-Information (DMI) to identify post-translational modulators M of a transcription factor TF by observing the changes in co-regulation (measured by Multi-Information) among a set of n target genes G_1⋯G_n in the presence or absence of the modulator M. My working hypothesis is that the set of target genes will be strongly co-regulated only when the modulator M is present, since the modulator will active the TF. The DMI algorithm requires in input a set of known target genes regulated by a common TF, and it returns in output a ranked list of predicted post-translational modulators of the TF. I first validated the approach using an “in-silico” datasets consisting of 100 GEPs and 760 genes. Next, I tested DMI performance on a real gene expression profile dataset, by identifying the post-translational modulators of 7 transcription factors for which I was able to collect a list of high-confident targets. This set of transcription factors included transcription factors such as P53, MYC and STAT3. Finally, as a case of study, I tested the DMI method on a transcription factor TFEB recently identified as a master regulator of lysosomal biogenesis and autophagy. By comparing the results of DMI with a High Content Screening (HCS) using siRNA oligo libraries against all the known phosphatases, I was able to show that DMI can achieve a very high precision. All these results confirm that DMI could be instrumental in identifying post-translational regulatory interactions in an efficient and cost-effective manner.


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