Figure 1.
The angiotensin II signalling pathway.
The angiotensin II signalling pathway can signal through G-protein dependent and G-protein independent pathways. The angiotensin receptor (AT1aR) is phosphorylated upon stimulation and G-proteins activated, which leads to an increase in IP3, intracellular calcium and diacyglycerol, which activates the signalling cascade involving several kinases e.g. PKC, MAPK and PKD. Phosphorylation of the receptor also leads to the recruitment of G-protein coupled receptor kinases and β-arrestin, and ultimately to internalisation of the receptor. Internalisation of the receptor yields the second wave of signalling, and has been associated with some of the positive effects of angiotensin II signalling in the cardiac setting.
Figure 2.
Comparison of the up-regulated proteins and phosphosites detected in the two MS studies.
The IPI IDs of the dataset from Xiao and co-workers was mapped to UniProt, and the two ID lists were compared by an in-house script. A) Comparison of the two SII Ang II regulated data sets revealed surprisingly little overlap in phosphosites, only 5%. On the protein level the overlap was merely 17%. The figure also shows that 15 phosphosites detected to be regulated only with Ang II in the Christensen study was regulated with SII Ang II in the study by Xiao and co-workers. Of these 15 phosphosites, 5 were more than 1.4 fold regulated, and was included as SII Ang II regulated in the analysis. B) The distribution of Ser, Tyr and Thr sites in the two studies.
Figure 3.
The percentage of proteins with multiple phosphosites in the angiotensin phosphoproteome is 57% and the number of proteins with multiple phosphosites decreases with increasing number of regulated phosphosites. The same distribution is observed when analysing the SII Ang II regulated subset.
Figure 4.
The flow of phosphosites and kinases.
Data was collected from Xiao et al., 2010 and Christensen et al., 2010. The combination of the two data sets resulted in a list of 1339 up-regulated phosphosites, which were used for prediction of activated kinases based on the NetworKIN algorithm. The 207 kinases in NetworKIN were reduced to 59, which were detected by mass spectrometry in the AT1aR-HEK293 proteome, had enough predictions for statistical analysis and a statistically significant p-value.
Figure 5.
Score distribution plots for the regulated versus non-regulated phosphosites of three kinases.
The score for each phosphosite was predicted by NetworKIN, both for regulated and non-regulated phosphosites. Due to the difference in sites in the two groups (10x), the counts were normalized to percentage. The likelihood of having a phosphosite of a given score is indicated by a bar, green indicates regulated phosphosites and red the non-regulated sites. In the top plot the score distributions are significantly different and in favour of the regulated phosphosites. Thus PKCδ is an active kinase. CK1γ3 resembles an inactive kinase, the score distributions are similarly and the p-value is non-significant. The bottom plot have a significantly different score distribution, however this is in favour of the non-regulated sites, and therefore an inactive kinase.
Figure 6.
Kinases involved in the angiotensin signalling pathway.
A comparison of the kinases phosphorylated in Christensen et al., 2010 and Xiao et al., 2010, with an overlay of the NetworKIN predicted active kinases. More than 30 of the kinases were differentially regulated on a phosphosite involved in kinase activity (indicated with *).
Table 1.
NetworKIN predicted active kinases.
Table 2.
The AT1aR regulated pathway maps.