Characterization of Benign Myocarditis Using Quantitative Delayed-Enhancement Imaging Based on Molli T1 Mapping

Delayed contrast enhancement after injection of a gadolinium-chelate (Gd-chelate) is a reference imaging method to detect myocardial tissue changes. Its localization within the thickness of the myocardial wall allows differentiating various pathological processes such as myocardial infarction (MI), inflammatory myocarditis, and cardiomyopathies. The aim of the study was first to characterize benign myocarditis using quantitative delayed-enhancement imaging and then to investigate whether the measure of the extracellular volume fraction (ECV) can be used to discriminate between MI and myocarditis.In 6 patients with acute benign myocarditis (32.2 ± 13.8 year-old, subepicardial late gadolinium enhancement [LGE]) and 18 patients with MI (52.3 ± 10.9 year-old, subendocardial/transmural LGE), myocardial T1 was determined using the Modified Look-Locker Imaging (MOLLI) sequence at 3 Tesla before and after Gd-chelate injection. T1 values were compared in LGE and normal regions of the myocardium. The myocardial T1 values were normalized to the T1 of blood, and the ECV was calculated from T1 values of myocardium and blood pre- and post-Gd injection.In both myocarditis and MI, the T1 was lower in LGE regions than in normal regions of the left ventricle. T1 of LGE areas was significantly higher in myocarditis than in MI (446.8 ± 45.8 vs 360.5 ± 66.9 ms, P = 0.003) and ECV was lower in myocarditis than in MI (34.5 ± 3.3 vs 53.8 ± 13.0 %, P = 0.004).Both inflammatory process and chronic fibrosis induce LGE (subepicardial in myocarditis and subendocardial in MI). The present study demonstrates that the determination of T1 and ECV is able to differentiate the 2 histological patterns.Further investigation will indicate whether the severity of ECV changes might help refine the predictive risk of LGE in myocarditis.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.     

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.