Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.

Project description:Background: Environmental lipopolysaccharide (LPS) and microbial component-enriched organic dusts cause significant lung diseases. These environmental exposures induce the recruitment and activation of distinct lung monocyte/macrophage subpopulations involved in disease pathogenesis. Given monocyte/macrophage activation is tightly linked to metabolism, the objective of these studies was to determine the role of the immunometabolic regulator, aconitate decarboxylase 1 (ACOD1), in environmental exposure-induced lung inflammation. Methods: Wild-type (WT) mice were intratracheally instilled (I.T.) with 10 ug LPS or saline. Whole lungs were profiled using bulk RNA sequencing or sorted to isolate monocyte/macrophage subpopulations. Sorted subpopulations were then characterized transcriptomically using a NanoString innate immunity multiplex array 48 hours post-exposure. Next, WT and Acod1-/- mice were instilled with LPS, 25% organic dust extract (ODE), or saline with serum, bronchoalveolar lavage fluid (BALF), and lung tissues collected. BALF metabolites of the tricarboxylic acid (TCA) cycle were quantified by mass-spectrometry. Cytokines/chemokines and tissue remodeling mediators were quantitated by ELISA. Lung immune cells were characterized by flow cytometry. Whole body plethysmography was performed 3 hours post-LPS with WT and Acod1-/- mice. Results: By bulk sequencing, Acod1 was one of the most significantly upregulated genes following LPS (vs. saline) exposure of murine whole lungs. Transcriptomic profiling of sorted lung monocyte/macrophage subpopulations corroborated Acod1 significance. Acod1-/- mice treated with LPS (vs. WT) demonstrated decreased BALF levels of itaconate, TCA cycle reprogramming, decreased BALF neutrophils, increased lung CD4+ T cells, decreased BALF and lung levels of TNF-a, and decreased BALF CXCL1. In comparison, Acod1-/- mice treated with ODE (vs. WT) demonstrated decreased serum pentraxin-2, BALF levels of itaconate, lung total cell, neutrophil, monocyte, and B cell infiltrates with decreased BALF levels of TNF-a, IL-6, and decreased lung CXCL1. Mediators of tissue remodeling (TIMP1, MMP8, MMP9) were also decreased in the LPS-exposed Acod1-/- mice, with MMP-9 also decreased in the ODE-exposed Acod1-/- mice. Lung function assessments demonstrated a blunted response to LPS-induced airway hyper-responsiveness in Acod1-/- mice. Conclusion: Acod1 is robustly upregulated in the lungs following LPS-exposure and encodes a key immunometabolic regulator. ACOD1 mediates the pro-inflammatory response to acute inhaled, environmental LPS and organic dust exposure-induced lung inflammation.

			Action	DRS
	BIOMD0000000936?filename=Ferrel2011_V2-biopax2.owl	Owl
	BIOMD0000000936?filename=Ferrel2011_V2-biopax3.owl	Owl
	BIOMD0000000936?filename=Ferrel2011_V2-matlab.m	Other
	BIOMD0000000936?filename=Ferrel2011_V2-octave.m	Other
	BIOMD0000000936?filename=Ferrel2011_V2.cps	Other

Dataset Information

Ferrel2011 - autonomous biochemical oscillator in cell cycle in Xenopus laevis v2

Dataset's files

Publications

Modeling the cell cycle: why do certain circuits oscillate?

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