BACKGROUND Excessive trabeculations and myocardial crypts are recurrent features across cardiomyopathies, yet their developmental origins and clinical significance remain poorly defined. To reveal the link between cardiac morphogenesis and disease, we generated humanized mouse models carrying patient-derived MYBPC3 frameshift mutations associated with overlapping hypertrophic cardiomyopathy (HCM) and left ventricular non-compaction (LVNC). METHODS We applied CRISPR-Cas9 to introduce distinct MYBPC3 frameshift alleles into the mouse genome and performed comprehensive phenotypic and transcriptomic profiling from fetal life through adulthood. RESULTS Adult homozygous Mybpc3 frameshift mutant mice like humans displayed hallmark HCM; however, without LVNC. Fetal and neonatal mutant hearts exhibited markedly enlarged ventricular trabeculae and crypts that progressed postnatally into the observed adult hypertrophy. Transcriptomic analysis revealed stage-specific dysregulation of oxidative metabolism, nonsense-mediated decay (NMD), and cell cycle pathways, peaking at postnatal days 1 and 7, indicating that these stages represent critical time points in disease onset. The persistent NMD signature, also observed in phenotype-negative heterozygotes, suggests a compensatory stress response. Enlarged trabeculae exhibited 2-fold increased trabecular cardiomyocyte proliferation, reversing the normal compact–trabecular proliferative gradient and leading to impaired ventricular compaction in neonates. Hey2 CreERT2 lineage tracing demonstrated invasion of Hey2 + compact cardiomyocytes into the trabeculae and ectopic trabecular expression of the Prdm16 transcription factor, indicating defective ventricular wall patterning and maturation. Postnatally, Hey2 + -derived cardiomyocytes became restricted to the outer/compact myocardium in mutants, while the inner/trabecular myocardium underwent accelerated hypertrophy concurrent with Prdm16 downregulation. Mice with a Mybpc3 missense variant also exhibited Hey2 + myocardial lineage expansion into trabeculae but no increased proliferation, implicating additional mechanisms beyond Hey2 regulation. Postnatal Prdm16 restoration, via transgenic expression in Mybpc3-null mice effectively attenuated hypertrophy, establishing a causal link between Mybpc3 loss, Prdm16 decline, and pathological remodeling. CONCLUSIONS Mybpc3 governs ventricular wall maturation by regulating cardiomyocyte proliferation, patterning, and maturation, partly via Prdm16. Disruption of these developmental programs precedes and drives adult HCM, highlighting a developmental role for sarcomeric proteins, and revealing postnatal Prdm16 modulation as an antihypertrophic therapeutic strategy.

Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis and prognosis difficult. Current echocardiography-based Artificial Intelligence (AI) models focus primarily on binary HFpEF detection in humans and do not provide comorbidity-specific phenotyping or temporal estimates of disease progression towards decompensation. We aimed to develop a unified AI framework, CardioMOD-Net, to perform multiclass diagnosis and continuous prediction of HFpEF onset directly from standard echocardiography cine loops in preclinical models. Methods: Mouse echocardiography videos from four groups were used: control (CTL), hyperglycaemic (HG), obesity (OB), and systemic arterial hypertension (SAH). Two-dimensional parasternal long-axis cine loops were decomposed using Higher Order Dynamic Mode Decomposition (HODMD) to extract temporal features for downstream analysis. A shared latent representation supported Vision Transformers, one for a classifier for diagnosis and another for a regression module for predicting the age at HFpEF onset. Results: Overall diagnostic accuracy across the four groups was 65 Discussion: This unified framework demonstrates that multiclass phenotyping and continuous HFpEF onset prediction can be obtained from a single cine loop, even under small-data conditions. The approach offers a foundation for integrating diagnostic and prognostic modelling in preclinical HFpEF research.

Dilated cardiomyopathy, defined by left ventricular dilatation and systolic dysfunction, is a major cause of heart failure, heart transplantation and sudden cardiac death, especially in young and middle-aged adults. Dilated cardiomyopathy of non-ischaemic aetiology is more common than once thought, with current prevalence estimated at around 1 in 220 based on cardiac magnetic resonance imaging studies, and the prevalence is twice as high in men than in women. However, the true prevalence could be even higher when early or subclinical forms of the disease are considered. Advances in genetic technologies over the past three decades have led to improved understanding of the genetic basis of non-ischaemic dilated cardiomyopathy and the identification of pathogenic variants in 30-40% of patients. The genetic architecture of this disease is complex and heterogeneous. Rather than being a strictly monogenic disorder, dilated cardiomyopathy results from a combination of monogenic and polygenic factors, along with gene-environment interactions as critical modifiers of disease penetrance and phenotype. A deeper understanding of the genetic factors and epidemiological landscape of dilated cardiomyopathy is crucial for improving clinical management and optimizing screening protocols and public health strategies.

In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the integration of data science techniques in this field presents significant challenges, as it requires a large volume of images, while ethical constraints, high costs, and variability in imaging protocols limit data acquisition. As a consequence, it is necessary to investigate different avenues to overcome this challenge. In this contribution, we offer an innovative tool to conquer this limitation. In particular, we delve into the application of a well recognized method known as the eigenfaces approach to classify cardiac diseases. This approach was originally motivated for efficiently representing pictures of faces using principal component analysis, which provides a set of eigenvectors (aka eigenfaces), explaining the variation between face images. Given its effectiveness in face recognition, we sought to evaluate its applicability to more complex medical imaging datasets. In particular, we integrate this approach with convolutional neural networks to classify echocardiography images taken from mice in five distinct cardiac conditions (healthy, diabetic cardiomyopathy, myocardial infarction, obesity and TAC hypertension). The results show a substantial and noteworthy enhancement when employing the singular value decomposition for pre-processing, with classification accuracy increasing by approximately 50%.

Heart diseases remain the leading cause of mortality worldwide, implying approximately 18 million deaths according to the WHO. In particular, heart failures (HF) press the healthcare industry to develop systems for their early, rapid, and effective prediction. This work presents an automatic system based on a novel framework which combines Modal Decomposition and Masked Autoencoders (MAE) to extend the application from heart disease classification to the more challenging and specific task of heart failure time prediction, not previously addressed to the best of authors' knowledge. This system comprises two stages. The first one transforms the data from a database of echocardiography video sequences into a large collection of annotated images compatible with the training phase of machine learning-based frameworks and deep learning-based ones. This stage includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm for both data augmentation and feature extraction. The second stage builds and trains a Vision Transformer (ViT). MAEs based on a combined scheme of self-supervised (SSL) and supervised learning, so far barely explored in the literature about heart failure prediction, are adopted to effectively train the ViT from scratch, even with scarce databases. The designed neural network analyses in real-time images from echocardiography sequences to estimate the time of happening a heart failure. This approach demonstrates to improve prediction accuracy from scarce databases and to be superior to several established ViT and Convolutional Neural Network (CNN) architectures. The source code will be incorporated into the next version release of the ModelFLOWs-app software (https://github.com/modelflows/ModelFLOWs-app).

The alternative calcineurin A variant CnAβ1 has a unique C-terminal domain that provides it with distinct subcellular localization and mechanism of action different from other calcineurin isoforms. Here, we used mice lacking CnAβ1’s C-terminal domain (CnAβ1Δi12) to show that the absence of this specific isoform strongly reprograms metabolism. CnAβ1Δi12 mice on a high-fat diet showed reduced body weight, white adipose tissue (WAT) mass, and circulating triglycerides, together with enhanced insulin sensitivity. In brown adipose tissue (BAT), CnAβ1 deficiency increased mitochondrial content and upregulated fatty acid oxidation and thermogenic proteins, improving cold resistance. Conversely, under starvation, CnAβ1Δi12 mice experienced rapid fat depletion and hypothermia. Importantly, BAT-specific FoxO1 knockout in CnAβ1Δi12 mice reduced catabolism-related gene expression and partially reversed the metabolic phenotypes, increasing body weight and WAT mass. Our findings reveal a relevant role for CnAβ1 in orchestrating BAT metabolism, highlighting its potential as a therapeutic target for obesity and metabolic syndrome. ### Competing Interest Statement J.S.W. has consulted for MyoKardia, Inc., Pfizer, Foresite Labs, Health Lumen, and Tenaya Therapeutics, and has received research support from Bristol Myers-Squibb. The rest of the authors declare no competing interests. Ministry of Science and Innovation, ES, PID2021-124629OB-I00, PLEC2022-009235, CEX2020-001041-S Comunidad de Madrid, https://ror.org/040scgh75, PEJ-2023-TL/SAL-GL-28706, PEJ-2019-TL/BMD-12831 Instituto de Salud Carlos III, PT23/00027, PT20/00044 British Heart Foundation, https://ror.org/02wdwnk04, FS/IPBSRF/22/27059, RE/18/4/34215, RG/19/6/34387, RE/18/4/34215 Sir Jules Thorn Charitable Trust, 21JTA Medical Research Council, MC\_UP\_1605/13

BACKGROUND:Truncating variants in the Filamin C gene (FLNCtv) are a frequent cause of genetic dilated cardiomyopathy (DCM) and non-dilated left ventricular cardiomyopathy (NDLVC), both characterized by arrhythmic complications and increased risk of sudden cardiac death. Currently, no gene-specific therapies exist for FLNCtv-induced cardiomyopathy. CRISPR activation (CRISPRa), which upregulates gene expression via transcriptional activation without cutting the genome, offers a promising strategy, particularly for genes like FLNC whose large size precludes conventional AAV-based gene replacement. However, CRISPRa has not yet been tested in vivo for cardiomyopathy treatment. METHODS:We generated a mouse model with a constitutive heterozygous deletion of FLNC exon 15 (FLNC-Ex15del/wt), mimicking a pathogenic human variant. We assessed cardiac expression of FLNC by qRT-PCR and western blot and evaluated electrical function via electrocardiography (ECG), including flecainide challenge. We designed a cardiacspecific, all-in-one AAV-CRISPRa system encoding a dead Cas9 linked to the transcription activation domain of VP64 (dSaCas9-VP64) under the cTnT promoter and sgRNAs previously optimised in HL-1 cardiomyocytes. The final construct was packaged in a myotropic AAVMYO capsid and systemically administered to FLNC mutant mice. Five weeks post-injection, qRT-PCR and ECGs and expression analyses were carried out to determine restoration of FLNC expression and rescue of ECG abnormalities. RESULTS:FLNC-Ex15del/wt mice exhibited no overt systolic dysfunction but showed significant ECG abnormalities, including prolonged QRS duration and reduced amplitude. Flecainide induced ventricular arrhythmias in ∼40% of mutant mice, further exacerbating ECG changes. Mutant hearts showed reduced FLNC mRNA and protein levels, consistent with haploinsufficiency. Transfection studies in HL-1 cells identified a highly effective sgRNA and scaffold combination, achieving up to 1.8-fold upregulation of endogenous FLNC expression. Systemic AAV delivery of the CRISPRa construct to mutant mice at 32 weeks restored FLNC mRNA to wild-type levels. Post-treatment ECGs showed increased QRS amplitude, and flecainide-induced arrhythmias were completely prevented in treated animals. CONCLUSIONS:Our heterozygous FLNCtv model partially recapitulates the electrical abnormalities observed in FLNCtv carriers with DCM/NDLVC, offering valuable insight into disease pathogenesis. Most importantly, this study provides the first in vivo demonstration that CRISPRa-AAV-mediated gene activation can effectively treat an inherited cardiomyopathy driven by haploinsufficiency. We show that even after disease onset, electrical dysfunction can be reversed through this targeted therapeutic approach. Our findings open new avenues for broader applications of CRISPRa-AAV platforms in addressing cardiac disorders rooted in insufficient gene dosage.

Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine learning- compatible collection of annotated images which can be used in the training phase of any kind of machine learning-based framework, including deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors' knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the efficacy of the HODMD algorithm and the superiority of the proposed system, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.

BACKGROUND:Arrhythmogenic right ventricular cardiomyopathy type 5 (ARVC5) is the most aggressive type of ARVC, caused by a fully penetrant missense mutation (p.S358L) in TMEM43 (transmembrane protein 43). Pathologically, the disease is characterized by dilation of the cardiac chambers and fibrofatty replacement of the myocardium, which results in heart failure and sudden cardiac death. Current therapeutic options are limited, and no specific therapies targeting the primary cause of the disease have been proposed. METHODS:We investigated whether overexpression of wild-type (WT) TMEM43 could overcome the detrimental effects of the mutant form. We used transgenic mouse models overexpressing either WT or mutant (S358L) TMEM43 to generate a double transgenic mouse line overexpressing both forms of the protein. In addition, we explored if systemic delivery of a codon-optimized self-complementary adeno-associated virus bearing WT-TMEM43 could improve disease progression assessed by ECG and echocardiography. RESULTS:Double transgenic mice overexpressing both WT and mutant TMEM43 forms showed delayed ARVC5 onset, improved cardiac contraction, and reduced ECG abnormalities compared with mice expressing S358L-TMEM43. In addition, cardiomyocyte death and myocardial fibrosis were reduced, with an overall increase in survival. Finally, we demonstrated that a single systemic administration of an adeno-associated virus carrying codon-optimized WT-TMEM43 prevents ventricular dysfunction and ECG abnormalities induced by S358L-TMEM43. CONCLUSIONS:Overexpression of WT-TMEM43 improves the pathological phenotype in a mouse model of ARVC5. Adeno-associated virus-mediated delivery of WT-TMEM43 offers a promising and specific therapy for patients suffering from this highly lethal disease.

In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. The HODMD algorithm is used first as feature extraction technique for the echocardiography datasets, taken from both healthy mice and mice afflicted by different cardiac diseases (Diabetic Cardiomyopathy, Obesity, TAC Hypertrophy and Myocardial Infarction). A total number of 130 echocardiography datasets are used in this work. The dominant features related to each cardiac disease were identified and represented by the HODMD algorithm as a set of DMD modes, which then are used as the input to the CNN. In a way, the database dimension was augmented, hence HODMD has been used, for the first time to the authors knowledge, for data augmentation in the machine learning framework. Six sets of the original echocardiography databases were hold out to be used as unseen data to test the performance of the CNN. In order to demonstrate the efficiency of the HODMD technique, two testcases are studied: the CNN is first trained using the original echocardiography images only, and second training the CNN using a combination of the original images and the DMD modes. The classification performance of the designed trained CNN shows that combining the original images with the DMD modes improves the results in all the testcases, as it improves the accuracy by up to 22%. These results show the great potential of using the HODMD algorithm as a data augmentation technique.

INTRODUCTION AND OBJECTIVES:Left ventricular reverse remodeling (LVRR) is a key therapeutic goal in dilated cardiomyopathy (DCM). However, its genetic predictors and prognostic impact remain uncertain. METHODS:We analyzed genotyped DCM patients with serial echocardiograms from the Spanish DCM study. The main objective was to assess the influence of genotype on LVRR, defined by improvement in ejection fraction within 12± 6 months. Secondary endpoints included major adverse cardiovascular events, end-stage heart failure (HF), and major ventricular arrhythmias. RESULTS:A total of 711 patients were included (67% male, mean age 50.8 years, baseline ejection fraction 31%, 44% genotype positive). LVRR occurred in 39% of genotype-positive vs 47% of genotype-negative patients (P=.036). Independent predictors of LVRR were TTN variants, lower baseline ejection fraction, and HF admission at diagnosis. In contrast, desmosomal, nuclear envelope and motor sarcomeric gene variants were associated with a lower likelihood of LVRR. During a median follow-up of 4.5 years, 26% of patients with initial LVRR showed subsequent deterioration, which was more frequent among genotype-positive individuals (32% vs 22%, P=.054). Compared with patients with sustained LVRR, those with deterioration had worse outcomes, including higher rates of major cardiovascular events (25% vs 7%), end-stage HF (18% vs 1%), and ventricular arrhythmia (12% vs 4%) (all P <.05). CONCLUSIONS:Genotype is a major determinant of both initial and long-term LVRR. Loss of ejection fraction improvement is common and strongly associated with adverse outcomes.

Myocardial remodeling including cardiomyocyte-death-independent, reactive fibrosis and disconnection of cardiomyocytes is at the basis of prevalent cardiac conditions converging into arrhythmias and heart failure. However, the molecular mechanisms behind these pathogenic responses remain incompletely understood limiting therapeutic opportunities. Here, we find that a molecular event common to unrelated heart diseases, namely the cleavage of the sarcomeric protein titin, is enough to trigger fast myocardial remodeling. Using an engineered system based on the expression of tobacco etch virus protease (TEVp) in mice, we show that 30% mosaic cardiac titin cleavage leads to global cardiomyocyte disengagement, activation of cardiac fibroblasts and interstitial collagen deposition. These effects are concurrent, involve ERK1/2 signaling, and are expected to contribute, at least, to myocardial remodeling in chemotherapy-induced cardiotoxicity and ischemia damage. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, 101002927 Ministerio de Ciencia, Innovación y Universidades (MCIU), FJC2021-047055-I European Molecular Biology Organization, ALTF 417-2022 La Caixa Foundation, LCF/BQ/DR22/11950024, LCF/PR/HR24/52440001

Background Following cardiac injury, whether the heart is permanently damaged or regenerating, distal organs are subjected to changes in physiological function. It remains largely unknown whether a cardiac lesion can affect gametes and transmit heritable changes to subsequent generations. Here, we report the influence of paternal cardiac injury on the following generation.Methods We studied the intergenerational influence of neonatal cardiac injury in the mouse, an animal model capable of regenerating the heart after early life injury. Neonatal male mice were subjected to ventricular cryoinjury, crossed at adulthood, and their sires were compared with litters derived from uninjured male mice. We used echocardiography, histology, and single nuclei RNA-sequencing to thoroughly characterize cardiac morphology, composition, function, and response to cardiac insult.Results We show that paternal cardiac injury affects the heart morphology of offspring under physiological conditions. Furthermore, in response to the same injury, the F1 generation derived from injured fathers shows better systemic and cardiac recovery, with non-pathological left ventricular enlargement and improved cardiac function during the regenerative process. This is accompanied by the activation of the immune system healing program at 3 weeks post-injury, together with enhanced transcription of genes associated with physiological hypertrophy.Conclusions The memory of a paternal neonatal lesion can be transmitted to offspring and improve their recovery from a cardiac insult.### Competing Interest StatementThe authors have declared no competing interest.