The Role of c-Myc in Regulating Cardiac Intermediary Metabolis

Background – The heart adapts in response to stress by inducing adaptive remodelling pathways leading to cardiac hypertrophy, however this is not sustained and with continued stress, maladaptive remodelling pathways ensue, impairing cell viability and contractile function leading to heart failure. An emerging concept is that cardiac hypertrophy is paralleled by changes in cardiomyocyte metabolism, which may themselves drive cardiac hypertrophy. The proto-oncogene c-Myc is a key regulator of cancer metabolism that promotes anabolic pathways driving tumorigenesis. Interestingly, c-Myc overexpression in the heart causes hypertrophy but how this is accomplished remains unclear.Objective – Define the functional role of c-Myc during remodelling of the adult heart with a focus on the relationship to intermediary glucose metabolism pathways that support anabolic growth in response to different types of hypertrophic stress stimuli.Methods – Cardiac-specific c-Myc knock-out (c-Myc KO) mice were generated and subjected to different types of stress stimuli. Pathological stress was induced by abdominal aortic banding (AAB) or transverse aortic constriction (TAC) and compared to sham surgery. Physiological stress was induced by voluntary wheel running (VWR) exercise and compared with sedentary controls. Metabolic pathway activity was comprehensively assessed by optimising a stable isotope resolved metabolomics method, using stable isotope labelled glucose (13C6 glucose) in ex-vivo Langendorff perfused hearts of floxed control and cs-c-Myc KO mice.Results – The c-Myc KO mice appear phenotypically normal and do not exhibit any changes in cardiac structure or function at baseline. When subjected to chronic pressure overload, c-Myc KO mice show a similar decline in cardiac function and a similar extent of cardiac hypertrophy as their floxed littermates. After chronic pressure overload, there is a significant decrease in 13C label incorporation into TCA metabolites and its related amino acids in the cs-c-Myc KO mice. In parallel, metabolites related to the hexosamine biosynthesis pathway show an increased 13C label incorporation after pressure overload in the floxed mice which is attenuated in the c-Myc KO mice. When subjected to regular exercise, c-Myc KO mice develop cardiac hypertrophy to the same extent as the floxed mice. Exercise causes an increase in the pentose phosphate pathway activity in the floxed mice which is mitigated in the c-Myc KO mice. Exercised floxed mice showed an increased lactate uptake and utilisation into the TCA cycle whereas c-Myc KO mice had decreased reliance on lactate.Conclusion – Knock-out of c-Myc alters glucose contribution to the TCA cycle and affects the diversion of glycolytic intermediates into pathways of intermediary metabolism important for anabolic growth and adaptation to stress. These results provide new insight into the rewiring of glucose carbon metabolism in the hypertrophied heart that is in part driven by c-Myc. Understanding adaptive remodelling pathways that drive cardiac hypertrophy may help lead to better treatments for preventing heart failure.<br/

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning

Myocardial slices as an in vitro platform to study cardiac disease

In vitro models are the pillars of fundamental research and drug discovery, offering reductionist methods to better understand cellular responses in isolation. Often these methods are oversimplified, which makes their relevance to human biology and clinical translation ambiguous. Living myocardial slices (LMSLMSs) are viable thin (200-400μm) cardiac tissue slices, with preserved native multicellularity, architecture, mechanical and electrophysiological responses. Recent development in their culture, by us and others, paved the way for long-term preservation of adult mammalian heart tissue in vitro, without significant changes in its function and structure. This model has been extensively used in healthy tissue; however, to date, there are no established pathological models to study disease progression in vitro. Here we hypothesised that LMSLMSs can be used as an informative in vitro disease model to study temporal and spatial changes in cardiac function/structure in response to local cardiac damage. Before inducing cardiac damage, we further improved and characterised the cultured LMS model by designing robust tissue holders, optimising the oxygenation of the media, and establishing the best slice thickness (300μ) for oxygen diffusion and tissue stability in culture. We found that the LMSLMSs were adequately oxygenated in the inner layers and responded to mechanical stimuli with an increase in their contraction and hyperpolarisation of the mitochondrial membrane. We then developed a cryoinjury model, by applying a cooled probe on the LMSLMSs. We found that injury created a distinct necrotic area, surrounded by a border zone (BZ). The injury resulted in preserved force but electrical instability, with the presence of spontaneous contractions. Microscopic analysis of the BZ showed the presence of high numbers of spontaneous Ca2+ sparks, which could be affected by inhibiting the activation of Ca2+/calmodulin-dependent protein kinase II (CamKII). The inhibitory effect was more pronounced in endocardial LMSLMSs, showing transmural differences of CamKII under pathological conditions. Structural analysis of the BZ also showed an acute increase of the sarcomere length and loss of t-tubule density upon culture, that could also account for the arrhythmogenicity of the injured LMSLMSs. One application of therapeutic interventions on the model, by using extracellular vesicles (EVs), did not show any functional or molecular improvements. This thesis demonstrates the significance of using diseased LMSLMSs to study the way that local injury affects tissue stability, function, and structure. Further work is required to better understand the link between spontaneous Ca2+ and contraction events, as well as finding successful therapeutic interventions.Open Acces

A Generative Shape Compositional Framework: Towards Representative Populations of Virtual Heart Chimaeras

Generating virtual populations of anatomy that capture sufficient variability while remaining plausible is essential for conducting in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. Hence, missing/partially-overlapping anatomical information is often available across individuals in a population. We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. The proposed generative model can synthesise complete whole complex shape assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We applied this framework to build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz. virtual chimaeras). We also propose a novel self supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images available in the UK Biobank. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity. This demonstrates the superiority of the proposed approach as the synthesised cardiac virtual populations are more plausible and capture a greater degree of variability in shape than those generated by the PCA-based shape model.Comment: 15 pages, 4 figure

The contact electrogram and its architectural determinants in atrial fibrillation

The electrogram is the sine qua non of excitable tissues, yet classification in atrial fibrillation (AF) remains poorly related to substrate factors. The objective of this thesis was to establish the relationship between electrograms and two commonly implicated substrate factors, connexin 43 and fibrosis in AF. The substrates and methods chosen to achieve this ranged from human acutely induced AF using open chest surgical mapping (Chapter 6), ex vivo whole heart Langendorff (Chapter 7) with in vivo telemetry confirming spontaneous AF in a new species of rat, the Brown Norway and finally isolated atrial preparations from an older cohort of rats using orthogonal pacing and novel co-localisation methods at sub-millimetre resolution and in some atria, optical mapping (Chapter 8). In rodents, electrode size and spacing was varied (Chapters 5, 10) to study its effects on structure function correlations (Chapter 9). Novel indices of AF organisation and automated electrogram morphology were used to quantify function (Chapter 4). Key results include the discoveries that humans without any history of prior AF have sinus rhythm electrograms with high spectral frequency content, that wavefront propagation velocities correlated with fibrosis and connexin phosphorylation ratios, that AF heterogeneity of conduction correlates to fibrosis and that orthogonal pacing in heavily fibrosed atria causes anisotropy in electrogram-fibrosis correlations. Furthermore, fibrosis and connexin 43 have differing and distinct spatial resolutions in their relationship with AF organisational indices. In conclusion a new model of AF has been found, and structure function correlations shown on an unprecedented scale, but with caveats of electrode size and direction dependence. These findings impact structure function methods and prove the effect of substrate on AF organisation.Open Acces