The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

The MH1 domain of Smad3 interacts with Pax6 and represses autoregulation of the Pax6 P1 promoter

Pax6 transcription is under the control of two main promoters (P0 and P1), and these are autoregulated by Pax6. Additionally, Pax6 expression is under the control of the TGFβ superfamily, although the precise mechanisms of such regulation are not understood. The effect of TGFβ on Pax6 expression was studied in the FHL124 lens epithelial cell line and was found to cause up to a 50% reduction in Pax6 mRNA levels within 24 h. Analysis of luciferase reporters showed that Pax6 autoregulation of the P1 promoter, and its induction of a synthetic promoter encoding six paired domain-binding sites, were significantly repressed by both an activated TGFβ receptor and TGFβ ligand stimulation. Subsequently, a novel Pax6 binding site in P1 was shown to be necessary for autoregulation, indicating a direct influence of Pax6 protein on P1. In transfected cells, and endogenously in FHL124 cells, Pax6 co-immunoprecipitated with Smad3 following TGFβ receptor activation, while in GST pull-down experiments, the MH1 domain of Smad3 was observed binding the RED sub-domain of the Pax6 paired domain. Finally, in DNA adsorption assays, activated Smad3 inhibited Pax6 from binding the consensus paired domain recognition sequence. We hypothesize that the Pax6 autoregulatory loop is targeted for repression by the TGFβ/Smad pathway, and conclude that this involves diminished paired domain DNA-binding function resulting from a ligand-dependant interaction between Pax6 and Smad3

Differential Gene Expression in High- and Low-Active Inbred Mice

Numerous candidate genes have been suggested in the recent literature with proposed roles in regulation of voluntary physical activity, with little evidence of these genes’ functional roles. This study compared the haplotype structure and expression profile in skeletal muscle and brain of inherently high- (C57L/J) and low- (C3H/HeJ) active mice. Expression of nine candidate genes [Actn2, Actn3, Casq1, Drd2, Lepr, Mc4r, Mstn, Papss2, and Glut4 (a.k.a. Slc2a4)] was evaluated via RT-qPCR. SNPs were observed in regions of Actn2, Casq1, Drd2, Lepr, and Papss2; however, no SNPs were located in coding sequences or associated with any known regulatory sequences. In mice exposed to a running wheel, Casq1 (P=0.0003) and Mstn (P=0.002) transcript levels in the soleus were higher in the low-active mice. However, when these genes were evaluated in naïve animals, differential expression was not observed, demonstrating a training effect. Among naïve mice, no genes in either tissue exhibited differential expression between strains. Considering that no obvious SNP mechanisms were determined or differential expression was observed, our results indicate that genomic structural variation or gene expression data alone is not adequate to establish any of these genes’ candidacy or causality in relation to regulation of physical activity

Pulmonary artery stiffness is independently associated with right ventricular mass and function: a cardiac MR imaging study

Purpose: To determine the relationship between pulmonary artery (PA) stiffness and both right ventricular (RV) mass and function with cardiac magnetic resonance (MR) imaging.Materials and Methods: The study was approved by the local research ethics committee, and all participants gave written informed consent. Cardiac MR imaging was performed at 1.5 T in 156 healthy volunteers (63% women; age range, 19-61 years; mean age, 36.1 years). High-temporal-resolution phase-contrast imaging was performed in the main and right PAs. Pulmonary pulse wave velocity (PWV) was determined by the interval between arterial systolic upslopes. RV function was assessed with feature tracking to derive peak systolic strain and strain rate, as well as peak early-diastolic strain rate. RV volumes, ejection fraction (RVEF), and mass were measured from the cine images. The association of pulmonary PWV with RV function and mass was quantified with univariate linear regression. Interstudy repeatability was assessed with intraclass correlation.Results: The repeatability coefficient for pulmonary PWV was 0.96. Increases in pulmonary PWV and RVEF were associated with increases in age (r = 0.32, P < .001 and r = 0.18, P = .025, respectively). After adjusting for age (P = .090), body surface area (P = .073), and sex (P = .005), pulmonary PWV demonstrated an independent positive association with RVEF (r = 0.34, P = .026). Significant associations were also seen with RV mass (r = 0.41, P = .004), RV radial strain (r = 0.38, P =. 022), and strain rate (r = 0.35, P = .002), and independent negative associations were seen with radial (r = 0.27, P = .003), longitudinal (r = 0.40, P = .007), and circumferential (r = 0.31, P = .005) peak early-diastolic strain rate with the same covariates.Conclusion: Pulmonary PWV is reliably assessed with cardiac MR imaging. In subjects with no known cardiovascular disease, increasing PA stiffness is associated with increasing age and is also moderately associated with both RV mass and function after controlling for age, body surface area, and sex. (C) RSNA, 201

Correction for Kipkorir et al., "De Novo Cobalamin Biosynthesis, Transport, and Assimilation and Cobalamin-Mediated Regulation of Methionine Biosynthesis in Mycobacterium smegmatis"

Volume 203, no. 7, e00620-20, 2021, https://doi.org/10.1128/JB.00620-20. Page 1: This article was published on 8 March 2021 with Stephanie S. Dawes missing from the byline. The byline was updated in the version posted on 21 January 2022. The following corrections were also made in that version. Page 1, footnote box: The following footnote was added. “§Present address: Stephanie S. Dawes, Laboratory of Structural Biology, School of Biological Sciences and Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand.” Page 14, Acknowledgments, line 3: “and Stephanie Dawes for providing the DmetE cobK::hyg mutant” was deleted. Page 14, Acknowledgments, lines 1 and 2 from bottom: “(to V.M. and D.F.W.)” was changed to “(to S.S.D., V.M., and D.F.W.).

The impact of Mendelian sleep and circadian genetic variants in a population setting

Rare variants in ten genes have been reported to cause Mendelian sleep conditions characterised by extreme sleep duration or timing. These include familial natural short sleep (ADRB1, DEC2/BHLHE41, GRM1 and NPSR1), advanced sleep phase (PER2, PER3, CRY2, CSNK1D and TIMELESS) and delayed sleep phase (CRY1). The association of variants in these genes with extreme sleep conditions were usually based on clinically ascertained families, and their effects when identified in the population are unknown. We aimed to determine the effects of these variants on sleep traits in large population-based cohorts. We performed genetic association analysis of variants previously reported to be causal for Mendelian sleep and circadian conditions. Analyses were performed using 191,929 individuals with data on sleep and whole-exome or genome-sequence data from 4 population-based studies: UK Biobank, FINRISK, Health-2000-2001, and the Multi-Ethnic Study of Atherosclerosis (MESA). We identified sleep disorders from self-report, hospital and primary care data. We estimated sleep duration and timing measures from self-report and accelerometery data. We identified carriers for 10 out of 12 previously reported pathogenic variants for 8 of the 10 genes. They ranged in frequency from 1 individual with the variant in CSNK1D to 1,574 individuals with a reported variant in the PER3 gene in the UK Biobank. No carriers for variants reported in NPSR1 or PER2 were identified. We found no association between variants analyzed and extreme sleep or circadian phenotypes. Using sleep timing as a proxy measure for sleep phase, only PER3 and CRY1 variants demonstrated association with earlier and later sleep timing, respectively; however, the magnitude of effect was smaller than previously reported (sleep midpoint similar to 7 mins earlier and similar to 5 mins later, respectively). We also performed burden tests of protein truncating (PTVs) or rare missense variants for the 10 genes. Only PTVs in PER2 and PER3 were associated with a relevant trait (for example, 64 individuals with a PTV in PER2 had an odds ratio of 4.4 for being "definitely a morning person", P = 4x10(-8); and had a 57-minute earlier midpoint sleep, P = 5x10(-7)). Our results indicate that previously reported variants for Mendelian sleep and circadian conditions are often not highly penetrant when ascertained incidentally from the general population.Peer reviewe