RESUMO
Network biology aims to understand cell behavior through the analysis of underlying complex biomolecular networks. Inference of condition-specific interaction networks from epigenomic data enables the characterization of the structural plasticity that regulatory networks can acquire in different tissues of the same organism. From this perspective, uncovering specific patterns of variation by comparing network structure among tissues could provide insights into systems-level mechanisms underlying cell behavior. Following this idea, here we propose an empirical framework to analyze mammalian tissue-specific networks, focusing on characterizing and contrasting their structure and behavior in response to perturbations. We structurally represent the state of the cell/tissue by condition specific transcription factor networks generated using DNase-seq chromatin accessibility data, and we profile their systems behavior in terms of the structural robustness against random and directed perturbations. Using this framework, we unveil the structural heterogeneity existing among tissues at different levels of differentiation. We uncover a novel and conserved systems property of regulatory networks underlying embryonic stem cells (ESCs): in contrast to terminally differentiated tissues, the promiscuous regulatory connectivity of ESCs produces a globally homogeneous network resulting in increased structural robustness. We show that this property is associated with a more permissive, less restrictive chromatin accesibility state in ESCs. Possible biological consequences of this property are discussed.
Assuntos
Redes Reguladoras de Genes , Mamíferos/metabolismo , Fatores de Transcrição/metabolismo , Animais , Diferenciação Celular , Células-Tronco Embrionárias/metabolismo , Regulação da Expressão Gênica no Desenvolvimento , Humanos , Camundongos , Biologia de SistemasRESUMO
Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.
Assuntos
Redes Reguladoras de Genes , Genômica , Modelos Teóricos , Desenvolvimento Vegetal/genética , Arabidopsis/genética , Biologia Computacional/métodos , Genômica/métodos , Raízes de Plantas/genética , SoftwareRESUMO
Mycobacterium tuberculosis has developed resistance to anti-tuberculosis first-line drugs. Multidrug-resistant strains complicate the control of tuberculosis and have converted it into a worldwide public health problem. Mutational studies of target genes have tried to envisage the resistance in clinical isolates; however, detection of these mutations in some cases is not sufficient to identify drug resistance, suggesting that other mechanisms are involved. Therefore, the identification of new markers of susceptibility or resistance to first-line drugs could contribute (1) to specifically diagnose the type of M. tuberculosis strain and prescribe an appropriate therapy, and (2) to elucidate the mechanisms of resistance in multidrug-resistant strains. In order to identify specific genes related to resistance in M. tuberculosis, we compared the gene expression profiles between the pansensitive H37Rv strain and a clinical CIBIN:UMF:15:99 multidrug-resistant isolate using microarray analysis. Quantitative real-time PCR confirmed that in the clinical multidrug-resistant isolate, the esxG, esxH, rpsA, esxI, and rpmI genes were upregulated, while the lipF, groES, and narG genes were downregulated. The modified genes could be involved in the mechanisms of resistance to first-line drugs in M. tuberculosis and could contribute to increased efficiency in molecular diagnosis approaches of infections with drug-resistant strains.