Esteban, F. J. | Search bioRxiv

bioRxiv2018

Ventricular chamber-specific Pitx2 insufficiency leads to cardiac hypertrophy and arrhythmias

Genome-wide association studies (GWAS) have identified genetic risk variant adjacent to the homeobox transcription factor PITX2 in atrial fibrillation (AF) patients. Experimental studies demonstrated that Pitx2 insufficiency leads to cellular and molecular substrates that increased atrial arrhythmias susceptibility. Pitx2 expression is present not only in the atrial but also in the ventricular myocytes. This study aims to investigate if insufficiency of Pitx2 in the developing and adult ventricular chambers increased susceptibility to ventricular arrhythmias. Conditional Pitx2 loss-offunction ventricular chamber-specific (Mlc2v-Cre) mouse mutants were generated using Cre/loxP technology. Pitx2 insufficiency in the ventricular myocardium leads interventricular septal thickening during cardiogenesis but else mice are viable until adulthood. Adult Mlc2vCre+Pitx2-/- hearts display hypertrophic and dilated ventricular chambers. ECG recordings demonstrated that Mlc2vCre+Pitx2-/- mice display increased QT and QRS intervals. Molecular analyses demonstrate that repolarization but not depolarization is severely impaired in these mutants. Microarrays analysis identified mRNAs and microRNAs altered in Pitx2 ventricular-specific mutants and provide evidences for miR-1 and miR-148 deregulation which in turn modulate Klf4 and distinct ion channel expression linked to cardiac hypertrophy and long QT-like defects. Our data demonstrate that Pitx2 insufficiency play leads to cellular and molecular ventricular remodeling which results in hypertrophic and dilated ventricular chambers and electrophysiological defects resembling long QT syndrome.

developmental biology↗

bioRxiv2017

Sampling Stability And Processing Parameter-Dependent Characteristics Of The 3D Fractal Dimension As A Marker Of Structural Brain Complexity In Magnetic Resonance Images

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced i) repeated-measurement deviation clusters around the registration target, ii) strong bidirectional correlations among image analysis groups, and iii) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.

neuroscience↗