Background: Calorie restriction (CR) produces a number of health benefits and ameliorates diseases of aging such as type 2 diabetes. The components of the pathways downstream of CR may provide intervention points for developing therapeutics for treating diseases of aging. The NAD + -dependent protein deacetylase SIRT1 has been implicated as one of the key downstream regulators of CR in yeast, rodents, and humans. Small molecule activators of SIRT1 have been identified that exhibit efficacy in animal models of diseases typically associated with aging including type 2 diabetes. To identify molecular processes induced in the liver of mice treated with two structurally distinct SIRT1 activators, SIRT501 (formulated resveratrol) and SRT1720, for three days, we utilized a systems biology approach and applied Causal Network Modeling (CNM) on gene expression data to elucidate downstream effects of SIRT1 activation.

BackgroundHumans 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.ResultsWe 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.ConclusionsThe 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.

BackgroundCritical 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.ResultsTo 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.ConclusionsTo 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.

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology.

A Vibrio cholerae deletion mutant lacking VS2773, a parA partitioning gene homolog located in a parAB operon on the large chromosome, displays altered positioning of the large chromosome origin. Deletion of a second parA homolog on the large chromosome (VC2061) does not affect its origin positioning. The origin position of the small chromosome is unchanged by either or both of these deletions, suggesting that VC2773 function is specific to the replicon on which it is carried. VC2773 and VC2772 form a parABS system with inverted repeats found near the large chromosome origin.In recent years, it has become possible to study the internal organization of the bacterial cell in great detail. DNA positioning and movement have been studied in various bacteria, using both live-and fixed-cell models. While it is clear that the chromosome is highly organized within the cell, the mechanisms underlying these processes are still not fully understood (18). Studies of bacteria with single chromosomes, such as Escherichia coli, Bacillus subtilis, and Caulobacter crescentus, have revealed variations in origin positioning and the timing of this placement (13,24,29). In the multireplicon-containing alpha-proteobacteria Agrobacterium tumefaciens and Sinorhizobium meliloti, origins localize predominantly to the cell poles, although they remain nonoverlapping (15). Fogel and Waldor have studied origin positioning in the bipartite genome of Vibrio cholerae, using a live-cell model and a fluorescent repressor-operator system (6). Their findings of distinct positioning patterns and cell cycle timing of movement for the two origins were independently confirmed by Fiebig et al. (5a) and suggest that a separate segregation mechanism may exist for each chromosome.Early studies of bacterial DNA partitioning focused on the maintenance of low-copy-number plasmids, such as F factor, P1, and R1. The genetic loci responsible for plasmid partitioning encode members of the ParA and ParB protein families. Homologs of these genes have been discovered on many bacterial chromosomes, with the exception of E. coli and related enteric organisms, and work in model systems such as B. subtilis, C. crescentus, Streptomyces coelicolor, and Pseudomonas putida has demonstrated a role for ParA and ParB in chromosome segregation. However, the segregation defects in strains with mutants of these genes have often been mild, with stronger phenotypes manifested under specific growth conditions or during developmental processes such as sporulation (9,12,16,19,23).The Vibrio species studied to date all have a divided genome consisting of two chromosomes; each chromosome carries a parAB locus (1,10,21,26). The locus on the large chromosome is related more closely to other bacterial chromosomal par loci than that on the small chromosome, which bears homology to plasmid-carried loci (10). Given the distinct localization patterns of the replication origins in V. cholerae, the parAB loci are factors that may confer specificity in positioning. Vibrio species also have a secon...