Role of TAZ in cancer stem cells and Wnt signaling

The transcriptional co-activator TAZ, known Hippo transducer together with his paralogue YAP, has recently emerged as important player in processes like organ growth and tumorigenesis. Here we focused on two aspects of TAZ biology: the first regards the role of TAZ as molecular determinant of breast cancer stem cells (CSCs); the second is the characterization of TAZ as downstream mediator of Wnt signaling. Using a bioinformatic approach, we discovered that more-malignant/CSC-enriched primary breast cancers, compared to well-differentiated/non-methastatic tumors, display an elevated activity of TAZ (Part 1), and that this correlates with a poorer prognosis. TAZ protein levels and activity are elevated in prospective CSCs and they increase during tumor evolution toward malignancy, both in vitro and in poorly-differentiated primary breast tumors. Moreover, TAZ is required to sustain self-renewal and tumor-initiation capacities of breast cancer cells. These features make TAZ a determinat of several characteristic of breast CSCs and a attractive molecule for therapy. We also studied upstream regulators of TAZ and found that, independently of the Hippo pathway, TAZ is regulated and trascriptionally-activated by the Wnt cascade (Part 2), sheding lights on the modalitites by which cells respond to the Wnt growth factors. Mechanistically, in the absence of Wnt activity, the components of the β-catenin destruction complex - APC, Axin and GSK3 - are also required to keep TAZ at low levels because phosphorylated β-catenin bridges TAZ to its ubiquitin ligase complex. Upon Wnt signaling, escape of β-catenin from the destruction complex impairs TAZ degradation and leads to concomitant accumulation and activation of β-catenin and TAZ

Effect of fibre orientation and bulk modulus on the electromechanical modelling of human ventricles

This work concerns the mathematical and numerical modeling of the heart. The aim is to enhance the understanding of the cardiac function in both physiological and pathological conditions. Along this road, a challenge arises from the multi-scale and multi-physics nature of the mathematical problem at hand. In this paper, we propose an electromechanical model that, in bi-ventricle geometries, combines the monodomain equation, the Bueno-Orovio minimal ionic model, and the Holzapfel-Ogden strain energy function for the passive myocardial tissue modelling together with the active strain approach combined with a model for the transmurally heterogeneous thickening of the myocardium. Since the distribution of the electric signal is dependent on the fibres orientation of the ventricles, we use a Laplace-Dirichlet Rule-Based algorithm to determine the myocardial fibres and sheets configuration in the whole bi-ventricle. In this paper, we study the influence of different fibre directions and incompressibility constraint and penalization on the compressibility of the material (bulk modulus) on the pressure-volume relation simulating a full heart beat. The coupled electromechanical problem is addressed by means of a fully segregated scheme. The numerical discretization is based on the Finite Element Method for the spatial discretization and on Backward Differentiation Formulas for the time discretization. The arising non-linear algebraic system coming from application of the implicit scheme is solved through the Newton method. Numerical simulations are carried out in a patient-specific biventricle geometry to highlight the most relevant results of both electrophysiology and mechanics and to compare them with physiological data and measurements. We show how various fibre configurations and bulk modulus modify relevant clinical quantities such as stroke volume, ejection fraction and ventricle contractility

A Reproducible Protocol to Assess Arrhythmia Vulnerability in Silico: Pacing at the End of the Effective Refractory Period

In both clinical and computational studies, different pacing protocols are used to induce arrhythmia and non-inducibility is often considered as the endpoint of treatment. The need for a standardized methodology is urgent since the choice of the protocol used to induce arrhythmia could lead to contrasting results, e.g., in assessing atrial fibrillation (AF) vulnerabilty. Therefore, we propose a novel method—pacing at the end of the effective refractory period (PEERP)—and compare it to state-of-the-art protocols, such as phase singularity distribution (PSD) and rapid pacing (RP) in a computational study. All methods were tested by pacing from evenly distributed endocardial points at 1 cm inter-point distance in two bi-atrial geometries. Seven different atrial models were implemented: five cases without specific AF-induced remodeling but with decreasing global conduction velocity and two persistent AF cases with an increasing amount of fibrosis resembling different substrate remodeling stages. Compared with PSD and RP, PEERP induced a larger variety of arrhythmia complexity requiring, on average, only 2.7 extra-stimuli and 3 s of simulation time to initiate reentry. Moreover, PEERP and PSD were the protocols which unveiled a larger number of areas vulnerable to sustain stable long living reentries compared to RP. Finally, PEERP can foster standardization and reproducibility, since, in contrast to the other protocols, it is a parameter-free method. Furthermore, we discuss its clinical applicability. We conclude that the choice of the inducing protocol has an influence on both initiation and maintenance of AF and we propose and provide PEERP as a reproducible method to assess arrhythmia vulnerability

An automate pipeline for generating fiber orientation and region annotation in patient specific atrial models

Modeling the \u27digital twin\u27 of a patient\u27s heart has gained traction in the last years and helps to understand the pathogenic mechanisms of cardiovascular disease to pave the way for personalized therapies. Although a 3D patient-specific model (PSM) can be obtained from computed tomography (CT) or magnetic resonance imaging (MRI), the fiber orientation of cardiac muscle, which significantly affects the electrophysiological and mechanical characteristics of the heart, can hardly be obtained in vivo. Several approaches have been suggested to solve this problem. However, most of them require a considerable amount of human interaction, which is both time-consuming and a potential source of error. In this work, a highly automated pipeline based on a Laplace-Dirichlet-rule-based method (LDRBM) for annotating fibers and anatomical regions in both atria is introduced. The calculated fiber arrangement was regionally compared with anatomical observations from literature and faithfully reproduced clinical and experimental data

Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG—A Large-Scale Computational Study Covering Anatomical Variability

The arrhythmogenesis of atrial fibrillation is associated with the presence of fibrotic atrial tissue. Not only fibrosis but also physiological anatomical variability of the atria and the thorax reflect in altered morphology of the P wave in the 12-lead electrocardiogram (ECG). Distinguishing between the effects on the P wave induced by local atrial substrate changes and those caused by healthy anatomical variations is important to gauge the potential of the 12-lead ECG as a non-invasive and cost-effective tool for the early detection of fibrotic atrial cardiomyopathy to stratify atrial fibrillation propensity. In this work, we realized 54,000 combinations of different atria and thorax geometries from statistical shape models capturing anatomical variability in the general population. For each atrial model, 10 different volume fractions (0–45%) were defined as fibrotic. Electrophysiological simulations in sinus rhythm were conducted for each model combination and the respective 12-lead ECGs were computed. P wave features (duration, amplitude, dispersion, terminal force in V1) were extracted and compared between the healthy and the diseased model cohorts. All investigated feature values systematically in- or decreased with the left atrial volume fraction covered by fibrotic tissue, however value ranges overlapped between the healthy and the diseased cohort. Using all extracted P wave features as input values, the amount of the fibrotic left atrial volume fraction was estimated by a neural network with an absolute root mean square error of 8.78%. Our simulation results suggest that although all investigated P wave features highly vary for different anatomical properties, the combination of these features can contribute to non-invasively estimate the volume fraction of atrial fibrosis using ECG-based machine learning approaches

Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence

Aims The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. Methods and results Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5–6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. Conclusion The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins