Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.     

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot–savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm³. With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.     

Binding mode of conformations and structure-based pharmacophore development for farnesyltransferase inhibitors

Farnesyltransferase (FTase) is one of the prenyltransferase family enzymes that catalyse the transfer of 15-membered isoprenoid (farnesyl) moiety to the cysteine of CAAX motif-containing proteins including Rho and Ras family of G proteins. Inhibitors of FTase act as drugs for cancer, malaria, progeria and other diseases. In the present investigation, we have developed two structure-based pharmacophore models from protein–ligand complex (3E33 and 3E37) obtained from the protein data bank. Molecular dynamics (MD) simulations were performed on the complexes, and different conformers of the same complex were generated. These conformers were undergone protein–ligand interaction fingerprint (PLIF) analysis, and the fingerprint bits have been used for structure-based pharmacophore model development. The PLIF results showed that Lys164, Tyr166, TrpB106 and TyrB361 are the major interacting residues in both the complexes. The RMSD and RMSF analyses on the MD-simulated systems showed that the absence of FPP in the complex 3E37 has significant effect in the conformational changes of the ligands. During this conformational change, some interactions between the protein and the ligands are lost, but regained after some simulations (after 2 ns). The structure-based pharmacophore models showed that the hydrophobic and acceptor contours are predominantly present in the models. The pharmacophore models were validated using reference compounds, which significantly identified as HITs with smaller RMSD values. The developed structure-based pharmacophore models are significant, and the methodology used in this study is novel from the existing methods (the original X-ray crystallographic coordination of the ligands is used for the model building). In our study, along with the original coordination of the ligand, different conformers of the same complex (protein–ligand) are used. It concluded that the developed methodology is significant for the virtual screening of novel molecules on different targets.