Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141 , IGF1R , TTN , and TNKS. Several loci where variants were deemed insignificant in univariate genome-wide association analyses are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we found strong gene co-expression correlations between these statistical epistasis contributors in healthy hearts and a significant connectivity decrease in failing hearts. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R . Our results expand the scope of genetic regulation of cardiac structure to epistasis.

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection

OBJECTIVES

Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The main aim of this study was to assess the performance of a previously developed DL model, trained on images from a tertiary center, using fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. A secondary aim was to compare initial screening diagnosis, which made use of live imaging at the point-of-care, with diagnosis by clinicians evaluating only stored images.

METHODS

All pregnancies with isolated severe CHD in the Northwestern region of The Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region and time period. We compared the accuracy of the initial clinical diagnosis (made in real time with access to live imaging) with that of the model (which had only stored imaging available) and with the performance of three blinded human experts who had access only to the stored images (like the model). We analyzed performance according to ultrasound study characteristics, such as duration and quality (scored independently by investigators), number of stored images and availability of screening views.

RESULTS

A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity, 53%). Model sensitivity and specificity were 91% and 78%, respectively. Blinded human experts (n = 3) achieved mean ± SD sensitivity and specificity of 55 ± 10% (range, 47-67%) and 71 ± 13% (range, 57-83%), respectively. There was a statistically significant difference in model correctness according to expert-graded image quality (P = 0.03). The abnormal cases included 19 lesions that the model had not encountered during its training; the model's performance in these cases (16/19 correct) was not statistically significantly different from that for previously encountered lesions (P = 0.41).

CONCLUSIONS

A previously trained DL algorithm had higher sensitivity than initial clinical assessment in detecting CHD in a cohort in which over 50% of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions to which it had not been exposed previously. Furthermore, when both the model and blinded human experts had access to only stored images and not the full range of images available to a clinician during a live scan, the model outperformed the human experts. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141, IGF1R, TTN, and TNKS. Several loci not prioritized by univariate genome-wide association analysis are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we show that these interactions are preserved at the level of the cardiac transcriptome. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R. Our results expand the scope of genetic regulation of cardiac structure to epistasis.

ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?

While multidisciplinary collaboration in echocardiography is not new, machine learning has the potential to further improve it. In this transcript of the ASE 2023 Annual Feigenbaum lecture, advancements in foundation models are discussed, including their advantages, current disadvantages, and future potential for echocardiography.

Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.

Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.

ENRICHing medical imaging training sets enables more efficient machine learning

OBJECTIVE

Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a model's performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice, such metrics are not tailored to, and almost never used for, image datasets.

MATERIALS AND METHODS

To address this problem, we introduce ENRICH-Eliminate Noise and Redundancy for Imaging Challenges-a customizable method that prioritizes images based on how much diversity each image adds to the training set.

RESULTS

First, we show that medical datasets are special in that in general each image adds less diversity than in nonmedical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and without up-front data labeling. ENRICH outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets.

CONCLUSIONS

ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.

Domain-guided data augmentation for deep learning on medical imaging

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.

Gender-based time discrepancy in diagnosis of coronary artery disease based on data analytics of electronic medical records

Background

Women continue to have worse Coronary Artery Disease (CAD) outcomes than men. The causes of this discrepancy have yet to be fully elucidated. The main objective of this study is to detect gender discrepancies in the diagnosis and treatment of CAD.

Methods

We used data analytics to risk stratify ~32,000 patients with CAD of the total 960,129 patients treated at the UCSF Medical Center over an 8 year period. We implemented a multidimensional data analytics framework to trace patients from admission through treatment to create a path of events. Events are any medications or noninvasive and invasive procedures. The time between events for a similar set of paths was calculated. Then, the average waiting time for each step of the treatment was calculated. Finally, we applied statistical analysis to determine differences in time between diagnosis and treatment steps for men and women.

Results

There is a significant time difference from the first time of admission to diagnostic Cardiac Catheterization between genders (p-value = 0.000119), while the time difference from diagnostic Cardiac Catheterization to CABG is not statistically significant.

Conclusion

Women had a significantly longer interval between their first physician encounter indicative of CAD and their first diagnostic cardiac catheterization compared to men. Avoiding this delay in diagnosis may provide more timely treatment and a better outcome for patients at risk. Finally, we conclude by discussing the impact of the study on improving patient care with early detection and managing individual patients at risk of rapid progression of CAD.

Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma

OBJECTIVE

Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician-performed pediatric FAST.

METHODS

We developed and conducted a retrospective cohort analysis of a deep learning view classifier on real-world FAST studies performed on injured children less than 18 years old in two pediatric emergency departments by 30 different clinicians. FAST was randomly distributed to training, validation, and test datasets, 70:20:10; each child was represented in only one dataset. The primary outcome was view classifier accuracy for video clips and still frames.

RESULTS

There were 699 FAST studies, representing 4925 video clips and 1,062,612 still frames, performed by 30 different clinicians. The overall classification accuracy was 97.8% (95% confidence interval [CI]: 96.0-99.0) for video clips and 93.4% (95% CI: 93.3-93.6) for still frames. Per view still frames were classified with an accuracy: 96.0% (95% CI: 95.9-96.1) cardiac, 99.8% (95% CI: 99.8-99.8) pleural, 95.2% (95% CI: 95.0-95.3) abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) suprapubic.

CONCLUSION

A deep learning classifier can accurately predict pediatric FAST views. Accurate view classification is important for quality assurance and feasibility of a multi-stage deep learning FAST model to enhance the evaluation of injured children.

Visualizing omicron: COVID-19 deaths vs. cases over time

For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of 2022. South Africa, one of the first countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly different from prior waves. To more readily visualize the dynamics of cases and deaths, it is natural to plot deaths per million against cases per million. Unlike the time-series plots of cases or deaths that have become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Here we present and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.

An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease

Congenital heart disease (CHD) is the most common birth defect. Fetal screening ultrasound provides five views of the heart that together can detect 90% of complex CHD, but in practice, sensitivity is as low as 30%. Here, using 107,823 images from 1,326 retrospective echocardiograms and screening ultrasounds from 18- to 24-week fetuses, we trained an ensemble of neural networks to identify recommended cardiac views and distinguish between normal hearts and complex CHD. We also used segmentation models to calculate standard fetal cardiothoracic measurements. In an internal test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images), the model achieved an area under the curve (AUC) of 0.99, 95% sensitivity (95% confidence interval (CI), 84-99%), 96% specificity (95% CI, 95-97%) and 100% negative predictive value in distinguishing normal from abnormal hearts. Model sensitivity was comparable to that of clinicians and remained robust on outside-hospital and lower-quality images. The model's decisions were based on clinically relevant features. Cardiac measurements correlated with reported measures for normal and abnormal hearts. Applied to guideline-recommended imaging, ensemble learning models could significantly improve detection of fetal CHD, a critical and global diagnostic challenge.

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council

Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.

TREC and KREC profiling as a representative of thymus and bone marrow output in patients with various inborn errors of immunity

Primary immune deficiency (PID) disorders are clinically and molecularly heterogeneous diseases. T cell receptor excision circles (TRECs) and κ (kappa)-deleting excision circles (KRECs) are markers of T and B cell development, respectively. They are useful tools to assess T and B cell function and immune reconstitution and have been used for newborn screening for severe combined immunodeficiency disease (SCID) and agammaglobulinemia, respectively. Their profiles in several genetically confirmed PIDs are still lacking. The objective of this study was to determine TREC and KREC genomic profiling among various molecularly confirmed PIDs. We used real-time-quantitative polymerase chain reaction (RT-qPCR)-based triplex analysis of TRECs, KRECs and β-actin (ACTB) in whole blood genomic DNA isolated from 108 patients with molecularly confirmed PIDs. All agammaglobulinemia patients had low KREC counts. All SCIDs and Omenn syndrome patients secondary to mutations in RAG1, RAG2, DCLRE1C and NHEJ1 had low TREC and KREC counts. JAK3-deficient patients had normal KREC and the TREC count was influenced by the type of mutation. Early-onset ADA patients had low TREC and KREC counts. Four patients with zeta-chain-associated protein kinase 70 (ZAP70) had low TREC. All purine nucleoside phosphorylase (PNP) patients had low TREC. Combined immunodeficiency (CID) patients secondary to AK2, PTPRC, CD247, DCLREC1 and STAT1 had normal TREC and KREC counts. Most patients with ataxia-telangiectasia (AT) patients had low TREC and KREC, while most DOCK8-deficient patients had low TRECs only. Two of five patients with Wiskott-Aldrich syndrome (WAS) had low TREC counts as well as one patient each with bare lymphocyte syndrome (BLS) and chronic granulomatous disease. All patients with Griscelli disease, Chediak-Higashi syndrome, hyper-immunoglobulin (Ig)M syndrome and IFNGR2 had normal TREC and KREC counts. These data suggest that, in addition to classical SCID and agammaglobulinemia, TREC/KREC assay may identify ZAP70 patients and secondary target PIDs, including dedicator of cytokinesis 8 (DOCK8) deficiency, AT and some individuals with WAS and BLS.

Pregnancy complications and premature cardiovascular events among 1.6 million California pregnancies

Background

Cardiovascular complications of pregnancy present an opportunity to assess risk for subsequent cardiovascular disease. We sought to determine whether peripartum cardiomyopathy and hypertensive disorder of pregnancy subtypes predict future myocardial infarction, heart failure or stroke independent of one another and of other risks such as gestational diabetes, preterm birth and intrauterine growth restriction.

Methods and results

The California Healthcare Cost and Utilization Project database was used to identify all hospitalised pregnancies from 2005 to 2009, with follow-up through 2011, for a retrospective cohort study. Pregnancies, exposures, covariates and outcomes were defined by International Classification of Diseases, Ninth Revision codes. Among 1.6 million pregnancies (mean age 28 years; median follow-up time to event excluding censoring 2.7 years), 558 cases of peripartum cardiomyopathy, 123 603 hypertensive disorders of pregnancy, 107 636 cases of gestational diabetes, 116 768 preterm births and 23 504 cases of intrauterine growth restriction were observed. Using multivariable Cox proportional hazards models, peripartum cardiomyopathy was independently associated with a 39.2-fold increase in heart failure (95% CI 30.0 to 51.9), resulting in ~1 additional hospitalisation per 1000 person-years. There was a 13.0-fold increase in myocardial infarction (95% CI 4.1 to 40.9) and a 7.7-fold increase in stroke (95% CI 2.4 to 24.0). Hypertensive disorders of pregnancy were associated with 1.4-fold (95% CI 1.0 to 2.0) to 7.6-fold (95% CI 5.4 to 10.7) higher risk of myocardial infarction, heart failure and stroke, resulting in a maximum of ~1 additional event per 1000 person-years. Gestational diabetes, preterm birth and intrauterine growth restriction had more modest associations.

Conclusion

These findings support close monitoring of women with cardiovascular pregnancy complications for prevention of early cardiovascular events and study of mechanisms underlying their development.

Distinct myocardial lineages break atrial symmetry during cardiogenesis in zebrafish

The ultimate formation of a four-chambered heart allowing the separation of the pulmonary and systemic circuits was key for the evolutionary success of tetrapods. Complex processes of cell diversification and tissue morphogenesis allow the left and right cardiac compartments to become distinct but remain poorly understood. Here, we describe an unexpected laterality in the single zebrafish atrium analogous to that of the two atria in amniotes, including mammals. This laterality appears to derive from an embryonic antero-posterior asymmetry revealed by the expression of the transcription factor gene meis2b. In adult zebrafish hearts, meis2b expression is restricted to the left side of the atrium where it controls the expression of pitx2c, a regulator of left atrial identity in mammals. Altogether, our studies suggest that the multi-chambered atrium in amniotes arose from a molecular blueprint present before the evolutionary emergence of cardiac septation and provide insights into the establishment of atrial asymmetry.

Fast and accurate view classification of echocardiograms using deep learning

Echocardiography is essential to cardiology. However, the need for human interpretation has limited echocardiography’s full potential for precision medicine. Deep learning is an emerging tool for analyzing images but has not yet been widely applied to echocardiograms, partly due to their complex multi-view format. The essential first step toward comprehensive computer-assisted echocardiographic interpretation is determining whether computers can learn to recognize these views. We trained a convolutional neural network to simultaneously classify 15 standard views (12 video, 3 still), based on labeled still images and videos from 267 transthoracic echocardiograms that captured a range of real-world clinical variation. Our model classified among 12 video views with 97.8% overall test accuracy without overfitting. Even on single low-resolution images, accuracy among 15 views was 91.7% vs. 70.2–84.0% for board-certified echocardiographers. Data visualization experiments showed that the model recognizes similarities among related views and classifies using clinically relevant image features. Our results provide a foundation for artificial intelligence-assisted echocardiographic interpretation.

A computer model can accurately classify what heart anatomy is depicted on an ultrasound scan. Rima Arnaout from the University of California, San Francisco, and colleagues used a machine-learning technique to teach a computer to recognize different types of video and still images produced by echocardiogram tests. After training the computer with real scans gathered from patients with a range of heart conditions, the researchers showed that their model could correctly classify what heart anatomy was shown in videos with 98% accuracy. The accuracy for still images fell to 92%, but that was still above the 80% average achieved by four professional echocardiographers tested on the same dataset. Now that the researchers have achieved successful view classification, the next challenge, they say, will be comprehensive computer-assisted interpretation of echocardiogram results.

Neuregulin-1 is essential for nerve plexus formation during cardiac maturation

The Neuregulin‐1 (Nrg1)/ErbB pathway plays multiple, critical roles in early cardiac and nervous system development and has been implicated in both heart and nerve repair processes. However, the early embryonic lethality of mouse Nrg1 mutants precludes an analysis of Nrg1's function in later cardiac development and homeostasis. In this study, we generated a novel nrg1 null allele targeting all known isoforms of nrg1 in zebrafish and examined cardiac structural and functional parameters throughout development. We found that zebrafish nrg1 mutants instead survived until young adult stages when they exhibited reduced survivorship. This coincided with structural and functional defects in the developing juvenile and young adult hearts, as demonstrated by reduced intracardiac myocardial density, cardiomyocyte cell number, swimming performance and dysregulated heartbeat. Interestingly, nrg1 mutant hearts were missing long axons on the ventricle surface by standard length (SL) 5 mm, which preceded juvenile and adult cardiac defects. Given that the autonomic nervous system normally exerts fine control of cardiac output through this nerve plexus, these data suggest that Nrg1 may play a critical role in establishing the cardiac nerve plexus such that inadequate innervation leads to deficits in cardiac maturation, function and survival.

LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE

The burden of cardiovascular and metabolic diseases worldwide is staggering. The emergence of systems approaches in biology promises new therapies, faster and cheaper diagnostics, and personalized medicine. However, a profound understanding of pathogenic mechanisms at the cellular and molecular levels remains a fundamental requirement for discovery and therapeutics. Animal models of human disease are cornerstones of drug discovery as they allow identification of novel pharmacological targets by linking gene function with pathogenesis. The zebrafish model has been used for decades to study development and pathophysiology. More than ever, the specific strengths of the zebrafish model make it a prime partner in an age of discovery transformed by big-data approaches to genomics and disease. Zebrafish share a largely conserved physiology and anatomy with mammals. They allow a wide range of genetic manipulations, including the latest genome engineering approaches. They can be bred and studied with remarkable speed, enabling a range of large-scale phenotypic screens. Finally, zebrafish demonstrate an impressive regenerative capacity scientists hope to unlock in humans. Here, we provide a comprehensive guide on applications of zebrafish to investigate cardiovascular and metabolic diseases. We delineate advantages and limitations of zebrafish models of human disease and summarize their most significant contributions to understanding disease progression to date.

Recovery of adult zebrafish hearts for high-throughput applications

Use of the zebrafish model system for studying development, regeneration, and disease is expanding toward use of adult hearts for cell dissociation and purification of RNA, DNA, and proteins. All of these applications demand the rapid recovery of significant numbers of zebrafish hearts to avoid gene regulatory, metabolic, and other changes that begin after death. Adult zebrafish hearts are also required for studying heart structure for a variety of mutants and for studying heart regeneration. However, the traditional zebrafish heart dissection is slow and difficult and requires specialized tools, making large-scale dissection of adult zebrafish hearts tedious. Traditional methods also harbor the risk of damaging the heart during the dissection. Here, we describe a method for dissection of adult zebrafish hearts that is fast, reproducible, and preserves heart architecture. Furthermore, this method does not require specialized tools, is painless for the zebrafish, can be performed on fresh or fixed specimens, and can be performed on zebrafish as young as one month old. The approach described expands the use of adult zebrafish for cardiovascular research.

Actin binding GFP allows 4D in vivo imaging of myofilament dynamics in the zebrafish heart and the identification of Erbb2 signaling as a remodeling factor of myofibril architecture

RATIONALE

Dilated cardiomyopathy is a leading cause of congestive heart failure and a debilitating complication of antineoplastic therapies. Despite disparate causes for dilated cardiomyopathy, maladaptive cardiac remodeling and decreased systolic function are common clinical consequences, begging an investigation of in vivo contractile dynamics in development and disease, one that has been impossible to date.

OBJECTIVE

To image myocardial contractile filament dynamics in vivo and to assess potential causes of dilated cardiomyopathy in antineoplastic therapies targeting the epidermal growth factor receptor Erbb2.

METHODS AND RESULTS

We generated a transgenic zebrafish line expressing an actin-binding green fluorescent protein in cardiomyocytes, allowing an in vivo imaging of myofilaments. Analysis of this line revealed architectural differences in myofibrils of the distinct cardiomyocyte subtypes. We used this model to investigate the effects of Erbb2 signaling on myofibrillar organization because drugs targeting ERBB2 (HER2/NEU) signaling, a mainstay of breast cancer chemotherapy, cause dilated cardiomyopathy in many patients. High-resolution in vivo imaging revealed that Erbb2 signaling regulates a switch between a dense apical network of filamentous myofibrils and the assembly of basally localized myofibrils in ventricular cardiomyocytes.

CONCLUSIONS

Using this novel line, we compiled a reference for myofibrillar microarchitecture among myocardial subtypes in vivo and at different developmental stages, establishing this model as a tool to analyze in vivo cardiomyocyte contractility and remodeling for a broad range of cardiovascular questions. Furthermore, we applied this model to study Erbb2 signaling in cardiomyopathy. We show a direct link between Erbb2 activity and remodeling of myofibrils, revealing an unexpected mechanism with potentially important implications for prevention and treatment of cardiomyopathy.

Abstract 283: Nppa Marks A Subset Of Embryonic Cardiomyocytes Predestined To Form Trabeculae In Zebrafish

The development and maintenance of cardiac trabeculae is required for proper ventricular function. Aberrant trabeculation can cause heart failure, arrhythmia, and death. Yet the mechanisms controlling initiation of trabeculation are incompletely understood, partly due to a lack of markers for trabeculation during early embryogenesis. We hypothesized that natriuretic peptide A (nppa) marks trabeculae before cardiomyocytes leave the single-celled ventricular layer. While nppa has been studied in mammals, the external development of the translucent zebrafish embryo offers new insight into the earliest stages of trabeculation. We have found by in situ hybridization, and by creating a fluorescent transgenic zebrafish line, that nppa exclusively marks zebrafish cardiac trabeculae from their onset through adulthood. Live spinning disc confocal video microscopy shows that nppa:GFP-positive cells move into the trabecular layer starting at 60 hours post fertilization (hpf). GFP-positive trabeculae are seen by 5 days post fertilization (dpf) in normal larvae. However, in cardiac troponin T2a mutants and in erbB2 mutants _ both known not to trabeculate _ nppa is still expressed, but the nppa:GFP positive cells fail to form trabecular projections. In embryos injected with a morpholino against atrial myosin heavy chain 6, where weakened atrial contraction leads to weaker blood flow without affecting the ventricle directly, GFP-positive cells form trabeculae similar to uninjected controls. Using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs), we created nppa mutants causing frameshift deletions. Mutants have poorly contractile hearts at 3 dpf and have been crossed into transgenic reporter lines in order to specifically assess trabeculation. Together, these data suggest that nppa marks a subset of embryonic cardiomyocytes destined to form trabeculae, and that nppa loss of function causes cardiac defects.

Late Recognition of Malignant Vasovagal Syncope

A 21-year-old female with a history of seizures since the age of 5 presented for long-term electroencephalographic (EEG) monitoring, but was found instead to have neurocardiogenic syncope. Is it appropriate for this patient to get a pacemaker?

Genetic and physiologic dissection of the vertebrate cardiac conduction system

Vertebrate hearts depend on highly specialized cardiomyocytes that form the cardiac conduction system (CCS) to coordinate chamber contraction and drive blood efficiently and unidirectionally throughout the organism. Defects in this specialized wiring system can lead to syncope and sudden cardiac death. Thus, a greater understanding of cardiac conduction development may help to prevent these devastating clinical outcomes. Utilizing a cardiac-specific fluorescent calcium indicator zebrafish transgenic line, Tg(cmlc2:gCaMP)(s878), that allows for in vivo optical mapping analysis in intact animals, we identified and analyzed four distinct stages of cardiac conduction development that correspond to cellular and anatomical changes of the developing heart. Additionally, we observed that epigenetic factors, such as hemodynamic flow and contraction, regulate the fast conduction network of this specialized electrical system. To identify novel regulators of the CCS, we designed and performed a new, physiology-based, forward genetic screen and identified for the first time, to our knowledge, 17 conduction-specific mutations. Positional cloning of hobgoblin(s634) revealed that tcf2, a homeobox transcription factor gene involved in mature onset diabetes of the young and familial glomerulocystic kidney disease, also regulates conduction between the atrium and the ventricle. The combination of the Tg(cmlc2:gCaMP)(s878) line/in vivo optical mapping technique and characterization of cardiac conduction mutants provides a novel multidisciplinary approach to further understand the molecular determinants of the vertebrate CCS.

Zebrafish model for human long QT syndrome

Long QT syndrome (LQTS) is a disorder of ventricular repolarization that predisposes affected individuals to lethal cardiac arrhythmias. To date, an appropriate animal model of inherited LQTS does not exist. The zebrafish is a powerful vertebrate model used to dissect molecular pathways of cardiovascular development and disease. Because fundamental electrical properties of the zebrafish heart are remarkably similar to those of the human heart, the zebrafish may be an appropriate model for studying human inherited arrhythmias. Here we describe the molecular, cellular, and electrophysiological basis of a zebrafish mutant characterized by ventricular asystole. Genetic mapping and direct sequencing identify the affected gene as kcnh2, which encodes the channel responsible for the rapidly activating delayed rectifier K(+) current (I(Kr)). We show that complete loss of functional I(Kr) in embryonic hearts leads to ventricular cell membrane depolarization, inability to generate action potentials (APs), and disrupted calcium release. A small hyperpolarizing current restores spontaneous APs, implying wild-type function of other ionic currents critical for AP generation. Heterozygous fish manifest overt cellular and electrocardiographic evidence for delayed ventricular repolarization. Our findings provide insight into the pathogenesis of homozygous kcnh2 mutations and expand the use of zebrafish mutants as a model system to study human arrhythmias.

Containment of simian immunodeficiency virus infection: cellular immune responses and protection from rechallenge following transient postinoculation antiretroviral treatment

To better understand the viral and host factors involved in the establishment of persistent productive infection by primate lentiviruses, we varied the time of initiation and duration of postinoculation antiretroviral treatment with tenofovir (9-[2-(R)-(phosphonomethoxy)propyl]adenine) while performing intensive virologic and immunologic monitoring in rhesus macaques, inoculated intravenously with simian immunodeficiency virus SIVsmE660. Postinoculation treatment did not block the initial infection, but we identified treatment regimens that prevented the establishment of persistent productive infection, as judged by the absence of measurable plasma viremia following drug discontinuation. While immune responses were heterogeneous, animals in which treatment resulted in prevention of persistent productive infection showed a higher frequency and higher levels of SIV-specific lymphocyte proliferative responses during the treatment period compared to control animals, despite the absence of either detectable plasma viremia or seroconversion. Animals protected from the initial establishment of persistent productive infection were also relatively or completely protected from subsequent homologous rechallenge. Even postinoculation treatment regimens that did not prevent establishment of persistent infection resulted in downmodulation of the level of plasma viremia following treatment cessation, compared to the viremia seen in untreated control animals, animals treated with regimens known to be ineffective, or the cumulative experience with the natural history of plasma viremia following infection with SIVsmE660. The results suggest that the host may be able to effectively control SIV infection if the initial exposure occurs under favorable conditions of low viral burden and in the absence of ongoing high level cytopathic infection of responding cells. These findings may be particularly important in relation to prospects for control of primate lentiviruses in the settings of both prophylactic and therapeutic vaccination for prevention of AIDS.

The potential clinical utility of serum alpha-protryptase levels

BACKGROUND

Because biopsy criteria for diagnosing systemic mastocytosis are not precise, the value of serum alpha-protryptase levels in the work-up of suspected systemic mastocytosis should be considered.

OBJECTIVE

A retrospective analysis was performed on subjects with total tryptase serum levels that were high (>/=20 ng/mL), while beta-tryptase serum levels were normal (<1 ng/mL) or modestly elevated (1 to 5 ng/mL).

METHODS

Over a 3.5-year period, 52 qualifying specimens were identified from 1369 consecutive samples. The corresponding subjects were divided into those with suspected mastocytosis and those with suspected anaphylaxis. Subjects with suspected mastocytosis were subdivided into 3 subgroups on the basis of biopsy results (positive, negative, or not available). Subjects with suspected anaphylaxis were subdivided into living and deceased subgroups.

RESULTS

Among the 15 subjects who underwent biopsy, alpha-protryptase serum levels (the difference between directly-measured levels of serum total tryptase and beta-tryptase), when greater than 75 ng/mL (n = 9), were always associated with a positive biopsy result for systemic mastocytosis; levels from 20 to 75 ng/mL (n = 6) were associated with a positive biopsy result in 50% of subjects. alpha-Protryptase serum levels may be a more sensitive screening test than a bone marrow biopsy for this disorder. Also, elevated alpha-protryptase serum levels in some adult patients return to normal over time, suggesting that mast cell hyperplasia resolved in these patients. Finally, a high alpha-protryptase level may reveal anaphylaxis to be a presenting manifestation of systemic mastocytosis or mast cell hyperplasia.

CONCLUSION

Levels of serum alpha-protryptase, relative to those of beta-tryptase, appear to be useful in the diagnostic work-up and follow-up of subjects with suspected systemic mastocytosis.