Dataset Information

Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract.

ABSTRACT: Mucociliary clearance (MCC), considered as a collaboration of mucus secreted from goblet cells, the airway surface liquid layer, and the beating of cilia of ciliated cells, is the airways' defense system against airborne contaminants. Because the process is well described at the molecular level, we gathered the available information into a suite of comprehensive causal biological network (CBN) models. The suite consists of three independent models that represent (1) cilium assembly, (2) ciliary beating, and (3) goblet cell hyperplasia/metaplasia and that were built in the Biological Expression Language, which is both human-readable and computable. The network analysis of highly connected nodes and pathways demonstrated that the relevant biology was captured in the MCC models. We also show the scoring of transcriptomic data onto these network models and demonstrate that the models capture the perturbation in each dataset accurately. This work is a continuation of our approach to use computational biological network models and mathematical algorithms that allow for the interpretation of high-throughput molecular datasets in the context of known biology. The MCC network model suite can be a valuable tool in personalized medicine to further understand heterogeneity and individual drug responses in complex respiratory diseases.

SUBMITTER: Yepiskoposyan H

PROVIDER: S-EPMC6384416 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract.

Yepiskoposyan Hasmik H Talikka Marja M Vavassori Stefano S Martin Florian F Sewer Alain A Gubian Sylvain S Luettich Karsta K Peitsch Manuel Claude MC Hoeng Julia J

Frontiers in genetics 20190215

Mucociliary clearance (MCC), considered as a collaboration of mucus secreted from goblet cells, the airway surface liquid layer, and the beating of cilia of ciliated cells, is the airways' defense system against airborne contaminants. Because the process is well described at the molecular level, we gathered the available information into a suite of comprehensive causal biological network (CBN) models. The suite consists of three independent models that represent (1) cilium assembly, (2) ciliary ...[more]

PMID: 30828347

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Project description: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.

Project description:Background: Infants undergoing congenital cardiac surgery with cardiopulmonary bypass are at high risk for respiratory complications. As impaired airway mucociliary clearance (MCC) can potentially contribute to pulmonary morbidity, our study objective was to measure airway clearance in infants undergoing congenital cardiac surgery and examine correlation with clinical covariables that may impair airway clearance function. Materials and Methods: Airway clearance in infants was measured over 30 min using inhaled nebulized Technetium 99m sulfur colloid administered either via a nasal cannula or the endotracheal tube in intubated infants. This was conducted bedside with a portable gamma camera. No difficulty was encountered in positioning the gamma camera over the patient, and neither the camera nor the MCC scan interfered with routine medical care or caused any adverse events. Patient and perioperative variables were examined relative to the MCC measurements. Results: We prospectively enrolled 57 infants undergoing congenital cardiac surgery and conducted a single MCC scan per patient. MCC data from 42 patients were analyzable, including five pre-operative, 15 (40.5%) in the immediate post-operative period (days 1-2), and 22 (59.5%) were later post-operative (≥3 days). Pre-operative MCC was inversely proportional to days requiring post-operative mechanical ventilation (p = 0.006) and non-invasive positive pressure ventilation (p = 0.017). MCC was higher at later post-operative days (p = 0.002) with immediate post-operative MCC being lower (3%; 0-13%) than either pre-operative (21%; 4-25%) (p = 0.091) or later post-operative MCC (18%; 0-29%) (p = 0.054). Among the infants with low post-operative MCC, significantly more were pre-mature [5/19 (26%) vs. 0/18 (0%); p = 0.046], were intubated [14/19 (75%) vs. only 7/18 (39%); p = 0.033] and were receiving higher FiO2 (40%, 27-47% vs. 26%, 21-37%; p = 0.015). Conclusions: This is the first study to show that infants undergoing congenital cardiac surgery have impaired MCC. MCC appeared lowest in the immediate post-operative period. Worse MCC was associated with pre-maturity, mechanical ventilation, or receiving higher FiO2. These findings suggest MCC scans should be further explored for informing clinical decision making to improve post-surgical respiratory outcomes. The possible therapeutic benefit of airway clearance maneuvers for infants with poor MCC function should also be investigated.

Dataset Information

Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract.

Publications

Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract.

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