Construction of a computable cell proliferation network focused on non-diseased lung cells

<p>Abstract</p> <p>Background</p> <p>Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work.</p> <p>Results</p> <p>To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data.</p> <p>Conclusions</p> <p>To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.</p

HdmX stimulates Hdm2-mediated ubiquitination and degradation of p53

The RING finger proteins HdmX and Hdm2 share significant structural and functional similarity. Hdm2 is a member of the RING finger family of ubiquitin-protein ligases E3 and targets the tumor suppressor protein p53 for degradation. Although HdmX also binds to p53, HdmX does not induce p53 degradation. Moreover, HdmX has been reported to interfere with p53 degradation in overexpression experiments. To obtain insight into the mechanism by which HdmX interferes with p53 degradation, we studied the effect of HdmX on the E3 activity of Hdm2 in vitro. Surprisingly, this revealed that HdmX stimulates Hdm2-mediated ubiquitination of p53 and that HdmX facilitates ubiquitination of Hdm2 and vice versa. In addition, down-regulation of HdmX expression within cells results in the accumulation of both p53 and Hdm2. Because HdmX alone does not have appreciable E3 activity, these data indicate that HdmX acts as a stimulator, rather than as an inhibitor, of the E3 activity of Hdm2 and that, at least under certain conditions, HdmX is actively involved in the degradation of both p53 and Hdm2

Complete switch from Mdm2 to human papillomavirus E6-mediated degradation of p53 in cervical cancer cells

The E6 oncoprotein of human papillomaviruses (HPVs) that are associated with cervical cancer utilizes the cellular ubiquitin–protein ligase E6-AP to target the tumor suppressor p53 for degradation. In normal cells (i.e., in the absence of E6), p53 is also a target of the ubiquitin–proteasome pathway. Under these conditions, however, p53 degradation is mediated by Mdm2 rather than by E6-AP. Here we show in a mutational analysis that, surprisingly, the structural requirements of p53 to serve as a proteolytic substrate differ between E6 proteins derived from different HPV types and, as expected, between Mdm2 and E6 proteins in vitro and in vivo. Stable expression of such mutants in HPV-negative and HPV-positive cell lines demonstrates that in HPV-positive cancer cells, the E6-dependent pathway of p53 degradation is not only active but, moreover, is required for degradation of p53, whereas the Mdm2-dependent pathway is inactive. Because the p53 pathway was reported to be functional in HPV-positive cancer cells, this finding indicates clearly that the ability of the E6 oncoprotein to target p53 for degradation is required for the growth of HPV-positive cancer cells

Growth Suppression Induced by Downregulation of E6-AP Expression in Human Papillomavirus-Positive Cancer Cell Lines Depends on p53

The ubiquitin-protein ligase E6-AP is utilized by the E6 oncoprotein of human papillomaviruses (HPVs) associated with cervical cancer to target the tumor suppressor p53 for degradation. Here, we report that downregulation of E6-AP expression by RNA interference results in both the accumulation of p53 and growth suppression of the HPV-positive cervical cancer cell lines HeLa and SiHa. In addition, HeLa cells, in which p53 expression was suppressed by RNA interference, are significantly less sensitive to the downregulation of E6-AP expression with respect to growth suppression than parental HeLa cells. These data indicate that the anti-growth-suppressive properties of E6-AP in HPV-positive cells depend on its ability to induce p53 degradation

A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue

Exposure to environmental stressors such as cigarette smoke (CS) elicits a variety of biological responses in humans, including the induction of inflammatory responses. These responses are especially pronounced in the lung, where pulmonary cells sit at the interface between the body's internal and external environments. We combined a literature survey with a computational analysis of multiple transcriptomic data sets to construct a computable causal network model (the Inflammatory Process Network (IPN)) of the main pulmonary inflammatory processes. The IPN model predicted decreased epithelial cell barrier defenses and increased mucus hypersecretion in human bronchial epithelial cells, and an attenuated pro-inflammatory (M1) profile in alveolar macrophages following exposure to CS, consistent with prior results. The IPN provides a comprehensive framework of experimentally supported pathways related to CS-induced pulmonary inflammation. The IPN is freely available to the scientific community as a resource with broad applicability to study the pathogenesis of pulmonary disease

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue

BACKGROUND: Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate. RESULTS: We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung. CONCLUSIONS: The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells