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Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data.

Imamura H, Yachie N, Saito R, Ishihama Y, Tomita M - BMC Bioinformatics (2010)

Bottom Line: The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm) tended to be close together within this phosphorylation dynamics-based network.The network was then analyzed using graph theory and compared with five known signal-transduction pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.

ABSTRACT

Background: Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.

Results: We analyzed time-course phosphoproteome data obtained previously by liquid chromatography mass spectrometry with the stable isotope labeling using amino acids in cell culture (SILAC) method. This provides the relative phosphorylation activities of digested peptides at each of five time points after stimulating HeLa cells with epidermal growth factor (EGF). We initially calculated the correlations between the phosphorylation dynamics patterns of every pair of peptides and connected the strongly correlated pairs to construct a network. We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm) tended to be close together within this phosphorylation dynamics-based network. The network was then analyzed using graph theory and compared with five known signal-transduction pathways. The dynamics-based network was correlated with known signaling pathways in the NetPath and Phospho.ELM databases, and especially with the EGF receptor (EGFR) signaling pathway. Although the phosphorylation patterns of many proteins were drastically changed by the EGF stimulation, our results suggest that only EGFR signaling transduction was both strongly activated and precisely controlled.

Conclusions: The construction of a phosphorylation dynamics-based network provides a useful overview of condition-specific intracellular signal transduction using quantitative time-course phosphoproteome data under specific experimental conditions. Detailed prediction of signal transduction based on phosphoproteome dynamics remains challenging. However, since the phosphorylation profiles of kinase-substrate pairs on the specific pathway were localized in the dynamics-based network, our method will be a complementary strategy to explore new components of protein signaling pathways in combination with previous methods (including software) of predicting direct kinase-substrate relationships.

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Comparison of the dynamics-based network with known cellular signaling pathways. (A) Known epidermal growth factor receptor (EGFR) signal transduction pathways in the EGFR (NetPath) analysis obtained from the NetPath database. Phosphorylation reactions are indicated by directed arrows from protein kinases to their target substrate. Red protein nodes indicate that the phosphorylation dynamics of the peptide or peptides were included in the dynamics-based network with R > 0.99. (B) Density distribution for the shortest path lengths (SPLs) of all reachable two-phosphopeptide nodes in the dynamics-based network (bars). Each asterisk denotes a pair of proteins in the EGFR (NetPath) known signaling pathway data; the asterisk color denotes the SPL of the two proteins in the known signaling network, and the position of each asterisk denotes the SPL of the corresponding two phosphopeptides in the dynamics-based network. (C-G) Comparison of SPLs in the dynamics-based network with those in each of the five known signaling networks. (C) EGFR (NetPath), (D) All (NetPath), (E) All - EGFR (NetPath), (F) Kinases (Phospho.ELM), and (G) All (Phospho.ELM). In each panel, the SPLs of the two proteins in the known network were assigned to the bins indicated on the horizontal axis; for protein pairs in each bin, we calculated the SPLs of their corresponding peptide pairs in the dynamics-based network (only for the reachable peptide pairs in this network), and the resulting mean value is shown on the vertical axis. The bin labeled "All" also included unreachable pairs in the known network. (D) In the comparison with EGFR (NetPath), Pearson's correlation coefficient (R) was calculated without the "All" bin.
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Figure 2: Comparison of the dynamics-based network with known cellular signaling pathways. (A) Known epidermal growth factor receptor (EGFR) signal transduction pathways in the EGFR (NetPath) analysis obtained from the NetPath database. Phosphorylation reactions are indicated by directed arrows from protein kinases to their target substrate. Red protein nodes indicate that the phosphorylation dynamics of the peptide or peptides were included in the dynamics-based network with R > 0.99. (B) Density distribution for the shortest path lengths (SPLs) of all reachable two-phosphopeptide nodes in the dynamics-based network (bars). Each asterisk denotes a pair of proteins in the EGFR (NetPath) known signaling pathway data; the asterisk color denotes the SPL of the two proteins in the known signaling network, and the position of each asterisk denotes the SPL of the corresponding two phosphopeptides in the dynamics-based network. (C-G) Comparison of SPLs in the dynamics-based network with those in each of the five known signaling networks. (C) EGFR (NetPath), (D) All (NetPath), (E) All - EGFR (NetPath), (F) Kinases (Phospho.ELM), and (G) All (Phospho.ELM). In each panel, the SPLs of the two proteins in the known network were assigned to the bins indicated on the horizontal axis; for protein pairs in each bin, we calculated the SPLs of their corresponding peptide pairs in the dynamics-based network (only for the reachable peptide pairs in this network), and the resulting mean value is shown on the vertical axis. The bin labeled "All" also included unreachable pairs in the known network. (D) In the comparison with EGFR (NetPath), Pearson's correlation coefficient (R) was calculated without the "All" bin.

Mentions: We initially obtained 46 kinase-substrate reactions in the EGFR signaling pathway from NetPath [41] as the EGFR (NetPath) pathway (Figure 2A). Since the dynamics-based network in this study was constructed on a pilot scale by connecting only proteins with similar phosphorylation profiles, we did not use phosphatase-related reactions that are believed to have inversely correlated time-course profiles between the phosphatase and its substrate. In the EGFR (NetPath) pathway, 11 proteins (red nodes in Figure 2A) matched to the dynamics-based network; for these 11 proteins, 30 protein pairs had corresponding phosphopeptides that were reachable in the dynamics-based network. We compared the SPLs of these 30 protein pairs in the EGFR (NetPath) pathway with the SPLs of their corresponding phosphopeptide pairs in the dynamics-based network, and found a marked correlation between the two datasets (Figure 2B and 2C), suggesting that the dynamics-based network for the EGF stimulation accurately clustered proteins that are close to each other in the actual EGFR signaling pathway.


Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data.

Imamura H, Yachie N, Saito R, Ishihama Y, Tomita M - BMC Bioinformatics (2010)

Comparison of the dynamics-based network with known cellular signaling pathways. (A) Known epidermal growth factor receptor (EGFR) signal transduction pathways in the EGFR (NetPath) analysis obtained from the NetPath database. Phosphorylation reactions are indicated by directed arrows from protein kinases to their target substrate. Red protein nodes indicate that the phosphorylation dynamics of the peptide or peptides were included in the dynamics-based network with R > 0.99. (B) Density distribution for the shortest path lengths (SPLs) of all reachable two-phosphopeptide nodes in the dynamics-based network (bars). Each asterisk denotes a pair of proteins in the EGFR (NetPath) known signaling pathway data; the asterisk color denotes the SPL of the two proteins in the known signaling network, and the position of each asterisk denotes the SPL of the corresponding two phosphopeptides in the dynamics-based network. (C-G) Comparison of SPLs in the dynamics-based network with those in each of the five known signaling networks. (C) EGFR (NetPath), (D) All (NetPath), (E) All - EGFR (NetPath), (F) Kinases (Phospho.ELM), and (G) All (Phospho.ELM). In each panel, the SPLs of the two proteins in the known network were assigned to the bins indicated on the horizontal axis; for protein pairs in each bin, we calculated the SPLs of their corresponding peptide pairs in the dynamics-based network (only for the reachable peptide pairs in this network), and the resulting mean value is shown on the vertical axis. The bin labeled "All" also included unreachable pairs in the known network. (D) In the comparison with EGFR (NetPath), Pearson's correlation coefficient (R) was calculated without the "All" bin.
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Figure 2: Comparison of the dynamics-based network with known cellular signaling pathways. (A) Known epidermal growth factor receptor (EGFR) signal transduction pathways in the EGFR (NetPath) analysis obtained from the NetPath database. Phosphorylation reactions are indicated by directed arrows from protein kinases to their target substrate. Red protein nodes indicate that the phosphorylation dynamics of the peptide or peptides were included in the dynamics-based network with R > 0.99. (B) Density distribution for the shortest path lengths (SPLs) of all reachable two-phosphopeptide nodes in the dynamics-based network (bars). Each asterisk denotes a pair of proteins in the EGFR (NetPath) known signaling pathway data; the asterisk color denotes the SPL of the two proteins in the known signaling network, and the position of each asterisk denotes the SPL of the corresponding two phosphopeptides in the dynamics-based network. (C-G) Comparison of SPLs in the dynamics-based network with those in each of the five known signaling networks. (C) EGFR (NetPath), (D) All (NetPath), (E) All - EGFR (NetPath), (F) Kinases (Phospho.ELM), and (G) All (Phospho.ELM). In each panel, the SPLs of the two proteins in the known network were assigned to the bins indicated on the horizontal axis; for protein pairs in each bin, we calculated the SPLs of their corresponding peptide pairs in the dynamics-based network (only for the reachable peptide pairs in this network), and the resulting mean value is shown on the vertical axis. The bin labeled "All" also included unreachable pairs in the known network. (D) In the comparison with EGFR (NetPath), Pearson's correlation coefficient (R) was calculated without the "All" bin.
Mentions: We initially obtained 46 kinase-substrate reactions in the EGFR signaling pathway from NetPath [41] as the EGFR (NetPath) pathway (Figure 2A). Since the dynamics-based network in this study was constructed on a pilot scale by connecting only proteins with similar phosphorylation profiles, we did not use phosphatase-related reactions that are believed to have inversely correlated time-course profiles between the phosphatase and its substrate. In the EGFR (NetPath) pathway, 11 proteins (red nodes in Figure 2A) matched to the dynamics-based network; for these 11 proteins, 30 protein pairs had corresponding phosphopeptides that were reachable in the dynamics-based network. We compared the SPLs of these 30 protein pairs in the EGFR (NetPath) pathway with the SPLs of their corresponding phosphopeptide pairs in the dynamics-based network, and found a marked correlation between the two datasets (Figure 2B and 2C), suggesting that the dynamics-based network for the EGF stimulation accurately clustered proteins that are close to each other in the actual EGFR signaling pathway.

Bottom Line: The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm) tended to be close together within this phosphorylation dynamics-based network.The network was then analyzed using graph theory and compared with five known signal-transduction pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.

ABSTRACT

Background: Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.

Results: We analyzed time-course phosphoproteome data obtained previously by liquid chromatography mass spectrometry with the stable isotope labeling using amino acids in cell culture (SILAC) method. This provides the relative phosphorylation activities of digested peptides at each of five time points after stimulating HeLa cells with epidermal growth factor (EGF). We initially calculated the correlations between the phosphorylation dynamics patterns of every pair of peptides and connected the strongly correlated pairs to construct a network. We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm) tended to be close together within this phosphorylation dynamics-based network. The network was then analyzed using graph theory and compared with five known signal-transduction pathways. The dynamics-based network was correlated with known signaling pathways in the NetPath and Phospho.ELM databases, and especially with the EGF receptor (EGFR) signaling pathway. Although the phosphorylation patterns of many proteins were drastically changed by the EGF stimulation, our results suggest that only EGFR signaling transduction was both strongly activated and precisely controlled.

Conclusions: The construction of a phosphorylation dynamics-based network provides a useful overview of condition-specific intracellular signal transduction using quantitative time-course phosphoproteome data under specific experimental conditions. Detailed prediction of signal transduction based on phosphoproteome dynamics remains challenging. However, since the phosphorylation profiles of kinase-substrate pairs on the specific pathway were localized in the dynamics-based network, our method will be a complementary strategy to explore new components of protein signaling pathways in combination with previous methods (including software) of predicting direct kinase-substrate relationships.

Show MeSH