Dataset Information

Network Modeling Approach to Predict Myofibroblast Differentiation.

ABSTRACT: Fibrotic disease is a major cause of morbidity and mortality and is characterized by the transition of resident fibroblast cells into active myofibroblasts, identified by their expression of alpha smooth muscle actin. Myofibroblast differentiation is regulated by growth factor signaling and mechanical signals transduced through integrins, which converge at focal adhesion proteins (Src and FAK) and MAPK signaling, but lead to divergent outcomes. While details are known about individual pathways, little is known about their interactions. To this end, an ODE-based model of this cell signaling network was developed in parallel with in vitro experiments to analyze potential mechanisms of crosstalk and regulation of ?SMA production. We found that cells lacking Src or FAK produce significantly less or more ?SMA than wild type cells, respectively. Transforming growth factor beta 1 and fibroblast growth factor signal through ERK and MAPK p38 with different dynamic profiles to increase or decrease ?SMA expression, respectively. Our model effectively recreated ?SMA expression levels across a set of 22 experimental conditions and matched some features of transient phosphorylation of ERK and p38. These results support a potential mechanism for regulation of fibroblast differentiation: ?SMA production is promoted by active p38 and Src and opposed by ERK.

SUBMITTER: Schroer AK

PROVIDER: S-EPMC7560994 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Network Modeling Approach to Predict Myofibroblast Differentiation.

Schroer Alison K AK Ryzhova Larisa M LM Merryman W David WD

Cellular and molecular bioengineering 20140706 3

Fibrotic disease is a major cause of morbidity and mortality and is characterized by the transition of resident fibroblast cells into active myofibroblasts, identified by their expression of alpha smooth muscle actin. Myofibroblast differentiation is regulated by growth factor signaling and mechanical signals transduced through integrins, which converge at focal adhesion proteins (Src and FAK) and MAPK signaling, but lead to divergent outcomes. While details are known about individual pathways, ...[more]

PMID: 33072223

Similar Datasets

Project description:Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.

Project description:Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum.

Dataset Information

Network Modeling Approach to Predict Myofibroblast Differentiation.

Publications

Network Modeling Approach to Predict Myofibroblast Differentiation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure