Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFalpha-induced inflammatory responses

Abstract

Background: Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.

Methods: In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFalpha-induced inflammation. Next, the gene expression profiles of TNFalpha-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).

Results: The highly ranked cross-talks which are functionally relevant to the TNFalpha pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.

Conclusions: A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.

Figure 1

Schematic diagram for reconstructing the protein-protein association. This diagram shows the basic concept of the reconstruction of protein-protein association. On the protein level, the interactions between proteins from the well-known database and experimental data were extracted. However, this kind of interactions only reflects all possible static connections without stimulus-specific response or temporal changes. Our model includes the gene expression patterns from different time course to infer the dynamic protein-protein associations and networks, suggesting a more significant and realistic method for network reconstruction of the living organism.

Figure 2

Flowchart of the proposed method to construct the protein-protein association network (PPAN). This flowchart depicts the process to construct the protein-protein association network (PPAN) and the afterward investigations in this study. The four key steps of PPAN construction are described in details in the text. The rough PPAN is set up from steps (1) and (2), and the refinement is then performed in steps (3) and (4) to obtain the refined PPAN.

Figure 3

Counting for the cross-talk ranking values (CTRVs). CTRV is a ranking value which reflects the potential of a protein in connection with multiple pathways. (A) All proteins connected with protein A belong to the same pathway A. In this case, the CTRV of protein A will not be changed. (B) If protein A connects with protein B, which belongs to a different pathway, then both CTRVs of protein A and B will be added by one.

Figure 4

Refined PPANs for HUVEC under TNFα stress at different time stages. The illustration shows a time-series layout for each refined PPANs from 0 to 8 hour. Every refined PPAN is identified via a set of gene expression profile with five data points. In order to distinguish proteins involved in different signaling cascades, proteins belong to the same pathway are labeled with the same color. The progression of ever-changing associations obviously reveals that new connections continuously emerge, reflecting the fact that new signaling modules and function communities are involved in the endothelial inflammatory response to the TNFα stimulus. In addition, the top five hubs with highly connected degree are marked with larger font size. The numbers of nodes, edges and the highly connected hubs at different time stages are outlined in Table 2.

Figure 5

Investigations of the (A) TNFα-related pathway and (B) IL-1/TLR4-related pathways. Protein components which are ultimately responsible for the trigger of an inflammatory function are labeled with the same color in the pathway, and the thicker red edges represent the associations listed in Supplementary Table S2 [Additional file 3] and Supplementary Table S3 [Additional file 4].

Figure 6

Dynamic progression of refined PPANs for HUVEC under TNFα stress. Time series refined PPANs under the TNFα stress from 0 to 3 hours are presented to monitor the dynamic properties of association progression. Positions of nodes are rearranged based on approximately up/down-stream relationships of the proteins. The general signaling proteins are shown as elliptic nodes; square nodes represent the receptor proteins and diamond nodes represent the possible negative regulator proteins. Dash gray lines represent the associations which are related to negative regulators. The levels of gene expression are indicated by the node color, in which the red color means the gene expression at that time is higher than its gene expression without TNFα treatment and the green color means the gene expression at that time is lower than its gene expression without TNFα treatment. The complete and pellucid time series diagrams from 0 to 8 hours are presented in Supplementary Figures [Additional file 5].

Figure 7

Bow-tie structure under TNFα stress for multiple pathways in the inflammatory system. A specific architecture of TNFα-induced endothelial inflammatory system is extracted from the PPAN in which the core elements of the bow-tie structure are identified via the CTRV ranking algorithm. Upon ligand binding, various types of receptors on the cell membrane trigger different signaling pathways and activate the downstream corresponding transcription factors such as NFκB. NFκB then regulates the expression of genes involved in inflammatory responses. These kinds of gene expression will induce some particular biological mechanisms helping the host to defense the invading microorganisms. In addition, the translation of cytokines and some negatively regulatory proteins will play roles of feedback control to coordinate the balance in immunity.