We compared proteins including significantly changed phosphopeptide(s) detected by mass spec analysis in more than two models (Supplementary Number 3aCc), from which we determined core nodes possessing regular or high betweenness scores ( 0 or 2?SD) reflecting their effect sizes within the pathological protein network (Fig

We compared proteins including significantly changed phosphopeptide(s) detected by mass spec analysis in more than two models (Supplementary Number 3aCc), from which we determined core nodes possessing regular or high betweenness scores ( 0 or 2?SD) reflecting their effect sizes within the pathological protein network (Fig.?1, Step 3 3). Open in a separate window Fig. in variable regions of the brain, leading to multiple pathological and medical prototypes. The heterogeneity of FTLD could be one of the reasons avoiding development of disease-modifying therapy. We newly develop a mathematical method to analyze chronological changes of PPI networks with sequential big data from comprehensive phosphoproteome of four FTLD knock-in (KI) mouse models (PGRNR504X-KI, TDP43N267S-KI, VCPT262A-KI and CHMP2BQ165X-KI mice) together with four transgenic mouse models of Alzheimers disease (AD) and with APPKM670/671NL-KI mice at multiple time points. The new method discloses the common core pathological network across FTLD and AD, which is shared by mouse models and human being postmortem brains. Based on the prediction, we performed restorative intervention of the FTLD models, and confirmed amelioration of pathologies CEP-37440 and symptoms of four FTLD mouse models by interruption of the core molecule HMGB1, verifying the new mathematical method to forecast dynamic molecular networks. values were 0.05 in Welchs test (values 0.05 by post hoc BenjaminiCHochberg (BH) procedure for multiple hypothesis testing (Fig.?1, Step 1 1). Based on the integrated proteinCprotein connection (PPI) database, we generated 24 pathological protein networks in a total of each mouse model at each time point (Fig.?1, Step 2 2; Supplementary Number?2a, b). We compared proteins including significantly changed phosphopeptide(s) recognized by mass spec analysis in more than two models (Supplementary Number 3aCc), from which we selected core nodes possessing regular or high betweenness scores ( 0 or 2?SD) reflecting their effect sizes within the pathological protein network (Fig.?1, Step 3 3). Open in a separate window Fig. 1 Circulation chart of recognition of dynamic molecular network shared across Rabbit Polyclonal to GSPT1 ADs and FTLDs.Step 1: CEP-37440 significantly changed phosphorylation sites were selected from comprehensive phosphoproteome analysis with total cerebral cortex cells of four AD and four FTLD mouse models at CEP-37440 1, 3, and 6 months of age. Step 2 2: pathological protein network of each mouse model at each time point was generated based on PPI database as explained in the methods. Step 3 3: protein nodes changed in more than two models and possessing a high score ( 3?SD) of betweenness were designated while core nodes. Step 4 4: edges to connect closely related nodes were selected by correlation analysis of three 3D vectors in which ratios of disease/control ideals at 1, 3, 6 months were used as x, y, CEP-37440 z positions. We defined core edge when two 3D vectors of two nodes at the end experienced a high correlation (correlation value ?0.9). Step 5: core network was made based on core nodes and core edges. Step 6: core networks of AD and FTLD were compared to generate AD core network, FTLD core network, and AD-FTLD common core network. Small circles were selected only by ideals (ideals (ideals (small circle) but also by ideals (large circles) (Fig.?1). Moreover, we examined by permutation test how hardly ever we could expect 46.7% of commonness. We recognized 1965 phosphoproteins in total, irrespective of their changes in disease condition through our phosphoproteome analyses of eight mouse CEP-37440 models (ideals 0.05 in Welchs test with post hoc BH procedure, assisting the validity of the AD-FTLD core network. Second, physical relationships of edges in AD-FTLD core network were reconfirmed by MINT, which is a PPI database constructed based on physical connection and was employed for generating core network with this study. MINT centered.