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

ULTRASOUND-DETERMINED DIMENSIONS OF THE RENAL PARENCHYMA ULTRASOUND IN HEALTHY CHILDREN

In 195 with no manifest urinary tract disease, of me one to seven years of age,the ultrasound was used to determine the renal parenchvma (thickness at the genderinter-gender levels); their correlation with age the kidney dimensions was examined.The dynamic ratio between the parenchyma dimensions and those of the kidney wasanalyzed. The real-time mechanical sector scanner (ALOKA SSD 500) was used with convex probes of 3,5 and 5 MHz in the supine position and in the counter-lateralbody decubitus. The parenchyma dimensions kept on increasing continuously duringthe analyzed period, most intensely in the second and fifth years of age. Theparenchyma enlargement was in a better con-elation with the kidney growth than withthe childrens age. Still, there is an evident slight enlargement of the parenchymadimensions than that of the kidney dimensions

Nicotine flux and pharmacokinetics-based considerations for early assessment of nicotine delivery systems

In the past few years, technological advancements enabled the development of novel electronic nicotine delivery systems (ENDS). Several empirical measures such as “nicotine flux” are being proposed to evaluate the abuse liability potential of these products. We explored the applicability of nicotine flux for clinical nicotine pharmacokinetics (PK) and 52-week quit success from cigarettes for a wide range of existing nicotine delivery systems. We found that the differences in nicotine flux for various nicotine delivery systems are not related to changes in PK, as nicotine flux does not capture key physiological properties such as nicotine absorption rate. Further, the 52-week quit success and abuse liability potential of nicotine nasal sprays (high nicotine flux product), and nicotine inhalers (nicotine flux similar to ENDS) are low, suggesting that nicotine flux is a poor metric for the assessment of nicotine delivery systems. PK indices are more dependable for characterizing nicotine delivery systems, and a nicotine plasma CmaxTmax > 1 could improve 52-week quit success from cigarettes. However, a single metric may be inadequate to fully assess the abuse liability potential of nicotine delivery systems and needs to be further studied. A combination of in vitro and in silico approaches could potentially address the factors influencing the inhaled aerosol dosimetry and resulting PK of nicotine to provide early insights for ENDS assessments. Further research is required to understand nicotine dosimetry and PK for ad libitum product use, and abuse liability indicators of nicotine delivery systems. This commentary is intended to (1) highlight the need to think beyond a single empirical metric such as nicotine flux, (2) suggest potential PK-based metrics, (3) suggest the use of in vitro and in silico tools to obtain early insights into inhaled aerosol dosimetry for ENDS, and (4) emphasize the importance of considering comprehensive clinical pharmacology outcomes to evaluate nicotine delivery systems

Lung function Raw Data

<p>Three consecutive perturbations were performed to record acceptable measurements (coefficient of determination >0.95) for each individual subject. FlexiVent software (SCIREQ) was used to analyze and calculate lung mechanics parameters. Measurements arepresented as a data matrix. Briefly, it has the animal number information on the first row and the exposure group information in the second row. Then, the first column has the name of the lung function parameter measured.</p

Body weight Raw Data

<p>The file shows the animal number information on the first row and the exposure group information in the second row. Then, the first column has the study day information, and each body weight recorded (in grams) is recorded in corresponding cells.</p

BALF FACS analyses reports

<p>pdf reports of all BALF FACS analyses</p

Lung Histopathology and Histomorphometry Raw Data

<p>The file contains histopathology scores and histomorphometry measurements . Briefly, it has the exposure group information on the first row, the time point information on the second row, and the animal number information in the third row. Then, the first column has the name of the finding evaluated, or the parameter in the histomorphometry measured and the second column the level at which this was done.</p

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