Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

Identifying Mechanisms and Therapeutic Targets in Muscle using Bayesian Parameter Estimation with Conditional Variational Autoencoders

Cardiomyopathies, often caused by mutations in genes encoding muscle proteins, are traditionally treated by phenotyping hearts and addressing symptoms post irreversible damage. With advancements in genotyping, early diagnosis is now possible, potentially preventing such damage. However, the intricate structure of muscle and its myriad proteins make treatment predictions challenging. Here we approach the problem of estimating therapeutic targets for a mutation in mouse muscle using a spatially explicit half sarcomere muscle model. We selected 9 rate parameters in our model linked to both small molecules and cardiomyopathy-causing mutations. We then randomly varied these rate parameters and simulated an isometric twitch for each combination to generate a large training dataset. We used this dataset to train a Conditional Variational Autoencoder (CVAE), a technique used in Bayesian parameter estimation. Given simulated or experimental isometric twitches, this machine learning model is able to then predict the set of rate parameters which are most likely to yield that result. We then predict the set of rate parameters associated with both control and the cardiac Troponin C (cTnC) I61Q variant in mouse trabeculae and model parameters that recover the abnormal I61Q cTnC twitches.

SIGNIFICANCE

Machine learning techniques have potential to accelerate discoveries in biologically complex systems. However, they require large data sets and can be challenging in high dimensional systems such as cardiac muscle. In this study, we combined experimental measures of cardiac muscle twitch forces with mechanistic simulations and a newly developed mixture of Bayesian inference with neural networks (in autoencoders) to solve the inverse problem of determining the underlying kinetics for observed force generation by cardiac muscle. The autoencoders are trained on millions of simulations spanning parameter spaces that correspond to the mechanochemistry of cardiac sarcomeres. We apply the trained model to experimental data in order to infer parameters that can explain a diseased twitch and ways to recover it.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications

The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable to genetic engineering, and can be differentiated into most somatic cell types. iPSCs have been widely applied to model human development and diseases, perform drug screening, and develop cell therapies. In this review, we outline key developments in the iPSC field and highlight the immense versatility of the iPSC technology for in vitro modeling and therapeutic applications. We begin by discussing the pivotal discoveries that revealed the potential of a somatic cell nucleus for reprogramming and led to successful generation of iPSCs. We consider the molecular mechanisms and dynamics of somatic cell reprogramming as well as the numerous methods available to induce pluripotency. Subsequently, we discuss various iPSC-based cellular models, from mono-cultures of a single cell type to complex three-dimensional organoids, and how these models can be applied to elucidate the mechanisms of human development and diseases. We use examples of neurological disorders, coronavirus disease 2019 (COVID-19), and cancer to highlight the diversity of disease-specific phenotypes that can be modeled using iPSC-derived cells. We also consider how iPSC-derived cellular models can be used in high-throughput drug screening and drug toxicity studies. Finally, we discuss the process of developing autologous and allogeneic iPSC-based cell therapies and their potential to alleviate human diseases.

DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins

MOTIVATION

Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers.

RESULTS

To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lixiaokun2020/DrugMGR.

Exosomes secreted from induced pluripotent stem cell ameliorate the lipopolysaccharide induced neuroinflammatory response via lncRNA-0949

PURPOSE

To study the effect of exosomes derived from the induced pluripotent stem cells (iPSCs) in the neuroinflammatory response of microglia caused by lipopolysaccharide (LPS) and reveal the potential underlying mechanism.

METHODS

A permanent microglia cell line HMO6 was activated by LPS. The features of exosomes were analyzed by nano flow cytometry, Western blot and transmission electron microscope. The RNA-seq was used to analyze the difference of noncoding RNA profiles between iPSC-Exos and HMO6 derived exosomes and proved that long no-coding RNA (lncRNA-0949) was highly expressed in the iPSC-Exos. Activated HMO6 cells were cocultured with iPSC-Exos in which lncRNA-0949 was overexpressed, knocked down or normally expressed. Quantitative real-time polymerase chain reaction (RT-qPCR), Enzyme-Linked Immunosorbent Assay and Western blot assay were adopted to analyze RNA and protein expression of inflammatory factors in HMO6 cells.

RESULTS

The oxidative stress and inflammatory response of microglia were significantly attenuated with the iPSC derived exosomes treatment. LncRNA-0949 was effectively delivered into the HMO6 cells through the iPSC-Exos, which largely alleviated the production of malondialdehyde, IL-6, IL-1β and TNF-α in HMO6 cells. Overexpression of lncRNA-0949 could enhance the anti-inflammatory effect of the iPSC-Exos, and knock-down of lncRNA-0949 impaired this availability.

CONCLUSION

According to our results, lncRNA-0949 enriched exosomes from iPSC could potentially be used as a therapeutic strategy to prevent/treat neuroinflammatory diseases.

Enhancing aortic valve drug delivery with PAR2-targeting magnetic nano-cargoes for calcification alleviation

Calcific aortic valve disease is a prevalent cardiovascular disease with no available drugs capable of effectively preventing its progression. Hence, an efficient drug delivery system could serve as a valuable tool in drug screening and potentially enhance therapeutic efficacy. However, due to the rapid blood flow rate associated with aortic valve stenosis and the lack of specific markers, achieving targeted drug delivery for calcific aortic valve disease has proved to be challenging. Here we find that protease-activated-receptor 2 (PAR2) expression is up-regulated on the plasma membrane of osteogenically differentiated valvular interstitial cells. Accordingly, we develop a magnetic nanocarrier functionalized with PAR2-targeting hexapeptide for dual-active targeting drug delivery. We show that the nanocarriers effectively deliver XCT790-an anti-calcification drug-to the calcified aortic valve under extra magnetic field navigation. We demonstrate that the nano-cargoes consequently inhibit the osteogenic differentiation of valvular interstitial cells, and alleviate aortic valve calcification and stenosis in a high-fat diet-fed low-density lipoprotein receptor-deficient (Ldlr-/-) mouse model. This work combining PAR2- and magnetic-targeting presents an effective targeted drug delivery system for treating calcific aortic valve disease in a murine model, promising future clinical translation.

Understanding blood development and leukemia using sequencing-based technologies and human cell systems

Our current understanding of human hematopoiesis has undergone significant transformation throughout the years, challenging conventional views. The evolution of high-throughput technologies has enabled the accumulation of diverse data types, offering new avenues for investigating key regulatory processes in blood cell production and disease. In this review, we will explore the opportunities presented by these advancements for unraveling the molecular mechanisms underlying normal and abnormal hematopoiesis. Specifically, we will focus on the importance of enhancer-associated regulatory networks and highlight the crucial role of enhancer-derived transcription regulation. Additionally, we will discuss the unprecedented power of single-cell methods and the progression in using in vitro human blood differentiation system, in particular induced pluripotent stem cell models, in dissecting hematopoietic processes. Furthermore, we will explore the potential of ever more nuanced patient profiling to allow precision medicine approaches. Ultimately, we advocate for a multiparameter, regulatory network-based approach for providing a more holistic understanding of normal hematopoiesis and blood disorders.

PROX1 Inhibits PDGF-B Expression to Prevent Myxomatous Degeneration of Heart Valves

BACKGROUND

Cardiac valve disease is observed in 2.5% of the general population and 10% of the elderly people. Effective pharmacological treatments are currently not available, and patients with severe cardiac valve disease require surgery. PROX1 (prospero-related homeobox transcription factor 1) and FOXC2 (Forkhead box C2 transcription factor) are transcription factors that are required for the development of lymphatic and venous valves. We found that PROX1 and FOXC2 are expressed in a subset of valvular endothelial cells (VECs) that are located on the downstream (fibrosa) side of cardiac valves. Whether PROX1 and FOXC2 regulate cardiac valve development and disease is not known.

METHODS

We used histology, electron microscopy, and echocardiography to investigate the structure and functioning of heart valves from Prox1ΔVEC mice in which Prox1 was conditionally deleted from VECs. Isolated valve endothelial cells and valve interstitial cells were used to identify the molecular mechanisms in vitro, which were tested in vivo by RNAScope, additional mouse models, and pharmacological approaches. The significance of our findings was tested by evaluation of human samples of mitral valve prolapse and aortic valve insufficiency.

RESULTS

Histological analysis revealed that the aortic and mitral valves of Prox1ΔVEC mice become progressively thick and myxomatous. Echocardiography revealed that the aortic valves of Prox1ΔVEC mice are stenotic. FOXC2 was downregulated and PDGF-B (platelet-derived growth factor-B) was upregulated in the VECs of Prox1ΔVEC mice. Conditional knockdown of FOXC2 and conditional overexpression of PDGF-B in VECs recapitulated the phenotype of Prox1ΔVEC mice. PDGF-B was also increased in mice lacking FOXC2 and in human mitral valve prolapse and insufficient aortic valve samples. Pharmacological inhibition of PDGF-B signaling with imatinib partially ameliorated the valve defects of Prox1ΔVEC mice.

CONCLUSIONS

PROX1 antagonizes PDGF-B signaling partially via FOXC2 to maintain the extracellular matrix composition and prevent myxomatous degeneration of cardiac valves.

Reprogramming iPSCs to study age-related diseases: Models, therapeutics, and clinical trials

The unprecedented rise in life expectancy observed in the last decades is leading to a global increase in the ageing population, and age-associated diseases became an increasing societal, economic, and medical burden. This has boosted major efforts in the scientific and medical research communities to develop and improve therapies to delay ageing and age-associated functional decline and diseases, and to expand health span. The establishment of induced pluripotent stem cells (iPSCs) by reprogramming human somatic cells has revolutionised the modelling and understanding of human diseases. iPSCs have a major advantage relative to other human pluripotent stem cells as their obtention does not require the destruction of embryos like embryonic stem cells do, and do not have a limited proliferation or differentiation potential as adult stem cells. Besides, iPSCs can be generated from somatic cells from healthy individuals or patients, which makes iPSC technology a promising approach to model and decipher the mechanisms underlying the ageing process and age-associated diseases, study drug effects, and develop new therapeutic approaches. This review discusses the advances made in the last decade using iPSC technology to study the most common age-associated diseases, including age-related macular degeneration (AMD), neurodegenerative and cardiovascular diseases, brain stroke, cancer, diabetes, and osteoarthritis.

PHDs-seq: a large-scale phenotypic screening method for drug discovery through parallel multi-readout quantification

High-throughput phenotypic screening is a cornerstone of drug development and the main technical approach for stem cell research. However, simultaneous detection of activated core factors responsible for cell fate determination and accurate assessment of directional cell transition are difficult using conventional screening methods that focus on changes in only a few biomarkers. The PHDs-seq (Probe Hybridization based Drug screening by sequencing) platform was developed to evaluate compound function based on their transcriptional effects in a wide range of signature biomarkers. In this proof-of-concept demonstration, several sets of markers related to cell fate determination were profiled in adipocyte reprogramming from dermal fibroblasts. After validating the accuracy, sensitivity and reproducibility of PHDs-seq data in molecular and cellular assays, a panel of 128 signalling-related compounds was screened for the ability to induce reprogramming of keloid fibroblasts (KF) into adipocytes. Notably, the potent ATP-competitive VEGFR/PDGFR inhibitor compound, ABT869, was found to promote the transition from fibroblasts to adipocytes. This study highlights the power and accuracy of the PHDs-seq platform for high-throughput drug screening in stem cell research, and supports its use in basic explorations of the molecular mechanisms underlying disease development.

Transfer learning enables predictions in network biology

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Human disease models in drug development

Biomedical research is undergoing a paradigm shift towards approaches centred on human disease models owing to the notoriously high failure rates of the current drug development process. Major drivers for this transition are the limitations of animal models, which, despite remaining the gold standard in basic and preclinical research, suffer from interspecies differences and poor prediction of human physiological and pathological conditions. To bridge this translational gap, bioengineered human disease models with high clinical mimicry are being developed. In this Review, we discuss preclinical and clinical studies that benefited from these models, focusing on organoids, bioengineered tissue models and organs-on-chips. Furthermore, we provide a high-level design framework to facilitate clinical translation and accelerate drug development using bioengineered human disease models.

Genetics of aortic valve disease

PURPOSE OF REVIEW

Aortic valve disease is a leading global cause of morbidity and mortality, posing an increasing burden on society. Advances in next-generation technologies and disease models over the last decade have further delineated the genetic and molecular factors that might be exploited in development of therapeutics for affected patients. This review describes several advances in the molecular and genetic understanding of AVD, focusing on bicuspid aortic valve (BAV) and calcific aortic valve disease (CAVD).

RECENT FINDINGS

Genomic studies have identified a myriad of genes implicated in the development of BAV, including NOTCH1 , SMAD6 and ADAMTS19 , along with members of the GATA and ROBO gene families. Similarly, several genes associated with the initiation and progression of CAVD, including NOTCH1 , LPA , PALMD , IL6 and FADS1/2 , serve as the launching point for emerging clinical trials.

SUMMARY

These new insights into the genetic contributors of AVD have offered new avenues for translational disease investigation, bridging molecular discoveries to emergent pharmacotherapeutic options. Future studies aimed at uncovering new genetic associations and further defining implicated molecular pathways are fuelling the new wave of drug discovery.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Network-Guided Multiomic Mapping of Aortic Valve Calcification

Despite devastating clinical sequelae of calcific aortic valve disease that range from left ventricular remodeling to arrhythmias, heart failure, and early death, the molecular insights into disease initiation and progression are limited and pharmacotherapies remain unavailable. The pathobiology of calcific aortic valve disease is complex and comprehensive studies are challenging valvular calcification is heterogeneous and occurs preferentially on the aortic surface, along a fibrocalcific spectrum. Here, we review efforts to study (epi-)genomic, transcriptomic, proteomic, and metabolomic aspects of aortic valve calcification in combination with network medicine-/systems biology-based strategies to integrate multilayered omics datasets and prioritize druggable targets for experimental validation studies. Ultimately, such holistic approach efforts may open therapeutic avenues that go beyond invasive and costly valve replacement therapy.

Notch Signaling in the Vasculature: Angiogenesis and Angiocrine Functions

Formation of a functional blood vessel network is a complex process tightly controlled by pro- and antiangiogenic signals released within the local microenvironment or delivered through the bloodstream. Endothelial cells precisely integrate such temporal and spatial changes in extracellular signals and generate an orchestrated response by modulating signaling transduction, gene expression, and metabolism. A key regulator in vessel formation is Notch signaling, which controls endothelial cell specification, proliferation, migration, adhesion, and arteriovenous differentiation. This review summarizes the molecular biology of endothelial Notch signaling and how it controls angiogenesis and maintenance of the established, quiescent vasculature. In addition, recent progress in the understanding of Notch signaling in endothelial cells for controlling organ homeostasis by transcriptional regulation of angiocrine factors and its relevance to disease will be discussed.

Congenital aortic valve stenosis: from pathophysiology to molecular genetics and the need for novel therapeutics

Congenital aortic valve stenosis (AVS) is one of the most common valve anomalies and accounts for 3%-6% of cardiac malformations. As congenital AVS is often progressive, many patients, both children and adults, require transcatheter or surgical intervention throughout their lives. While the mechanisms of degenerative aortic valve disease in the adult population are partially described, the pathophysiology of adult AVS is different from congenital AVS in children as epigenetic and environmental risk factors play a significant role in manifestations of aortic valve disease in adults. Despite increased understanding of genetic basis of congenital aortic valve disease such as bicuspid aortic valve, the etiology and underlying mechanisms of congenital AVS in infants and children remain unknown. Herein, we review the pathophysiology of congenitally stenotic aortic valves and their natural history and disease course along with current management strategies. With the rapid expansion of knowledge of genetic origins of congenital heart defects, we also summarize the literature on the genetic contributors to congenital AVS. Further, this increased molecular understanding has led to the expansion of animal models with congenital aortic valve anomalies. Finally, we discuss the potential to develop novel therapeutics for congenital AVS that expand on integration of these molecular and genetic advances.

From novel discovery tools and biomarkers to precision medicine-basic cardiovascular science highlights of 2021/22

Here, we review the highlights of cardiovascular basic science published in 2021 and early 2022 on behalf of the European Society of Cardiology Council for Basic Cardiovascular Science. We begin with non-coding RNAs which have emerged as central regulators cardiovascular biology, and then discuss how technological developments in single-cell 'omics are providing new insights into cardiovascular development, inflammation, and disease. We also review recent discoveries on the biology of extracellular vesicles in driving either protective or pathogenic responses. The Nobel Prize in Physiology or Medicine 2021 recognized the importance of the molecular basis of mechanosensing and here we review breakthroughs in cardiovascular sensing of mechanical force. We also summarize discoveries in the field of atherosclerosis including the role of clonal haematopoiesis of indeterminate potential, and new mechanisms of crosstalk between hyperglycaemia, lipid mediators, and inflammation. The past 12 months also witnessed major advances in the field of cardiac arrhythmia including new mechanisms of fibrillation. We also focus on inducible pluripotent stem cell technology which has demonstrated disease causality for several genetic polymorphisms in long-QT syndrome and aortic valve disease, paving the way for personalized medicine approaches. Finally, the cardiovascular community has continued to better understand COVID-19 with significant advancement in our knowledge of cardiovascular tropism, molecular markers, the mechanism of vaccine-induced thrombotic complications and new anti-viral therapies that protect the cardiovascular system.

The Role of the Notch Signaling Pathway in Recovery of Cardiac Function after Myocardial Infarction

Myocardial infarction (MI) is a pathological process, evidencing as massive death of cardiomyocytes associated with hypoxic and oxidative stress. The formation of areas of fibrosis ultimately leads to heart failure. There are some mechanisms that contribute to the functional repair of the heart. In most mammals, including humans, the Notch signaling pathway has cardioprotective effects. It is involved in the formation of the heart in embryogenesis and in the restoration of cardiac function after MI due to: (1) reducing oxidative stress; (2) prevention of apoptosis; (3) regulation of inflammation; (4) containment of fibrosis and hypertrophy of cardiomyocytes; (5) tissue revascularization; and (6) regulation of proliferation and differentiation of cardiomyocytes. In addition, the Notch signaling pathway interacts with other signaling cascades involved in the pathogenesis of MI and subsequent cardiac repair. In this review, we consider the Notch signaling pathway as a potential target for therapeutic approaches aimed at improving cardiac recovery after MI.

Translational potential of hiPSCs in predictive modeling of heart development and disease

Congenital heart disease (CHD) represents a major class of birth defects worldwide and is associated with cardiac malformations that often require surgical intervention immediately after birth. Despite the intense efforts from multicentric genome/exome sequencing studies that have identified several genetic variants, the etiology of CHD remains diverse and often unknown. Genetically modified animal models with candidate gene deficiencies continue to provide novel molecular insights that are responsible for fetal cardiac development. However, the past decade has seen remarkable advances in the field of human induced pluripotent stem cell (hiPSC)-based disease modeling approaches to better understand the development of CHD and discover novel preventative therapies. The iPSCs are derived from reprogramming of differentiated somatic cells to an embryonic-like pluripotent state via overexpression of key transcription factors. In this review, we describe how differentiation of hiPSCs to specialized cardiac cellular identities facilitates our understanding of the development and pathogenesis of CHD subtypes. We summarize the molecular and functional characterization of hiPSC-derived differentiated cells in support of normal cardiogenesis, those that go awry in CHD and other heart diseases. We illustrate how stem cell-based disease modeling enables scientists to dissect the molecular mechanisms of cell-cell interactions underlying CHD. We highlight the current state of hiPSC-based studies that are in the verge of translating into clinical trials. We also address limitations including hiPSC-model reproducibility and scalability and differentiation methods leading to cellular heterogeneity. Last, we provide future perspective on exploiting the potential of hiPSC technology as a predictive model for patient-specific CHD, screening pharmaceuticals, and provide a source for cell-based personalized medicine. In combination with existing clinical and animal model studies, data obtained from hiPSCs will yield further understanding of oligogenic, gene-environment interaction, pathophysiology, and management for CHD and other genetic cardiac disorders.

Crenigacestat (LY3039478) inhibits osteogenic differentiation of human valve interstitial cells from patients with aortic valve calcification in vitro

Calcific aortic valve disease (CAVD) is one of the dangerous forms of vascular calcification. CAVD leads to calcification of the aortic valve and disturbance of blood flow. Despite high mortality, there is no targeted therapy against CAVD or vascular calcification. Osteogenic differentiation of valve interstitial cells (VICs) is one of the key factors of CAVD progression and inhibition of this process seems a fruitful target for potential therapy. By our previous study we assumed that inhibitors of Notch pathway might be effective to suppress aortic valve leaflet calcification. We tested CB-103 and crenigacestat (LY3039478), two selective inhibitors of Notch-signaling, for suppression of osteogenic differentiation of VICs isolated from patients with CAVD in vitro. Effect of inhibitors were assessed by the measurement of extracellular matrix calcification and osteogenic gene expression. For effective inhibitor (crenigacestat) we also performed MTT and proteomics study for better understanding of its effect on VICs in vitro. CB-103 did not affect osteogenic differentiation. Crenigacestat completely inhibited osteogenic differentiation (both matrix mineralization and Runx2 expression) in the dosages that had no obvious cytotoxicity. Using proteomics analysis, we found several osteogenic differentiation-related proteins associated with the effect of crenigacestat on VICs differentiation. Taking into account that crenigacestat is FDA approved for clinical trials for anti-tumor therapy, we argue that this drug could be considered as a potential inhibitor of cardiovascular calcification.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Modeling congenital heart disease: lessons from mice, hPSC-based models, and organoids

In this review, Rao et al. contrast mouse models of CHD, which maintain the anatomical arrangement of the heart, and human cellular models of CHD, which are more likely to capture human-specific biology but lack anatomical structure. They also discuss the recent development of cardiac organoids, which are a promising step toward more anatomically informative human models of CHD.

Congenital heart defects (CHDs) are among the most common birth defects, but their etiology has long been mysterious. In recent decades, the development of a variety of experimental models has led to a greater understanding of the molecular basis of CHDs. In this review, we contrast mouse models of CHD, which maintain the anatomical arrangement of the heart, and human cellular models of CHD, which are more likely to capture human-specific biology but lack anatomical structure. We also discuss the recent development of cardiac organoids, which are a promising step toward more anatomically informative human models of CHD.

Functional genomics and the future of iPSCs in disease modeling

Induced pluripotent stem cells (iPSCs) are valuable in disease modeling because of their potential to expand and differentiate into virtually any cell type and recapitulate key aspects of human biology. Functional genomics are genome-wide studies that aim to discover genotype-phenotype relationships, thereby revealing the impact of human genetic diversity on normal and pathophysiology. In this review, we make the case that human iPSCs (hiPSCs) are a powerful tool for functional genomics, since they provide an in vitro platform for the study of population genetics. We describe cutting-edge tools and strategies now available to researchers, including multi-omics technologies, advances in hiPSC culture techniques, and innovations in drug development. Functional genomics approaches based on hiPSCs hold great promise for advancing drug discovery, disease etiology, and the impact of genetic variation on human biology.

Application of hiPSC as a Drug Tester Via Mimicking a Personalized Mini Heart

Human induced pluripotent stem cells (hIPSC) have been used to produce almost all types of human cells currently, which makes them into several potential applications with replicated patient-specific genotype. Furthermore, hIPSC derived cardiomyocytes assembled engineering heart tissue can be established to achieve multiple functional evaluations by tissue engineering technology. This short review summarized the current advanced applications based on the hIPSC derived heart tissue in molecular mechanisms elucidating and high throughput drug screening.

Population-based high-throughput toxicity screen of human iPSC-derived cardiomyocytes and neurons

In this study, we establish a population-based human induced pluripotent stem cell (hiPSC) drug screening platform for toxicity assessment. After recruiting 1,000 healthy donors and screening for high-frequency human leukocyte antigen (HLA) haplotypes, we identify 13 HLA-homozygous "super donors" to represent the population. These "super donors" are also expected to represent at least 477,611,135 of the global population. By differentiating these representative hiPSCs into cardiomyocytes and neurons we show their utility in a high-throughput toxicity screen. To validate hit compounds, we demonstrate dose-dependent toxicity of the hit compounds and assess functional modulation. We also show reproducible in vivo drug toxicity results using mouse models with select hit compounds. This study shows the feasibility of using a population-based hiPSC drug screening platform to assess cytotoxicity, which can be used as an innovative tool to study inter-population differences in drug toxicity and adverse drug reactions in drug discovery applications.

NerLTR-DTA: drug-target binding affinity prediction based on neighbor relationship and learning to rank

MOTIVATION

Drug-target interaction prediction plays an important role in new drug discovery and drug repurposing. Binding affinity indicates the strength of drug-target interactions. Predicting drug-target binding affinity is expected to provide promising candidates for biologists, which can effectively reduce the workload of wet laboratory experiments and speed up the entire process of drug research. Given that, numerous new proteins are sequenced and compounds are synthesized, several improved computational methods have been proposed for such predictions, but there are still some challenges. (i) Many methods only discuss and implement one application scenario, they focus on drug repurposing and ignore the discovery of new drugs and targets. (ii) Many methods do not consider the priority order of proteins (or drugs) related to each target drug (or protein). Therefore, it is necessary to develop a comprehensive method that can be used in multiple scenarios and focuses on candidate order.

RESULTS

In this study, we propose a method called NerLTR-DTA that uses the neighbor relationship of similarity and sharing to extract features, and applies a ranking framework with regression attributes to predict affinity values and priority order of query drug (or query target) and its related proteins (or compounds). It is worth noting that using the characteristics of learning to rank to set different queries can smartly realize the multi-scenario application of the method, including the discovery of new drugs and new targets. Experimental results on two commonly used datasets show that NerLTR-DTA outperforms some state-of-the-art competing methods. NerLTR-DTA achieves excellent performance in all application scenarios mentioned in this study, and the rm(test)2 values guarantee such excellent performance is not obtained by chance. Moreover, it can be concluded that NerLTR-DTA can provide accurate ranking lists for the relevant results of most queries through the statistics of the association relationship of each query drug (or query protein). In general, NerLTR-DTA is a powerful tool for predicting drug-target associations and can contribute to new drug discovery and drug repurposing.

AVAILABILITY AND IMPLEMENTATION

The proposed method is implemented in Python and Java. Source codes and datasets are available at https://github.com/RUXIAOQING964914140/NerLTR-DTA.

Gene Editing in Pluripotent Stem Cells and Their Derived Organoids

With the rapid rise in gene-editing technology, pluripotent stem cells (PSCs) and their derived organoids have increasingly broader and practical applications in regenerative medicine. Gene-editing technologies, from large-scale nucleic acid endonucleases to CRISPR, have ignited a global research and development boom with significant implications in regenerative medicine. The development of regenerative medicine technologies, regardless of whether it is PSCs or gene editing, is consistently met with controversy. Are the tools for rewriting the code of life a boon to humanity or a Pandora's box? These technologies raise concerns regarding ethical issues, unexpected mutations, viral infection, etc. These concerns remain even as new treatments emerge. However, the potential negatives cannot obscure the virtues of PSC gene editing, which have, and will continue to, benefit mankind at an unprecedented rate. Here, we briefly introduce current gene-editing technology and its application in PSCs and their derived organoids, while addressing ethical concerns and safety risks and discussing the latest progress in PSC gene editing. Gene editing in PSCs creates visualized in vitro models, providing opportunities for examining mechanisms of known and unknown mutations and offering new possibilities for the treatment of cancer, genetic diseases, and other serious or refractory disorders. From model construction to treatment exploration, the important role of PSCs combined with gene editing in basic and clinical medicine studies is illustrated. The applications, characteristics, and existing challenges are summarized in combination with our lab experiences in this field in an effort to help gene-editing technology better serve humans in a regulated manner. Current preclinical and clinical trials have demonstrated initial safety and efficacy of PSC gene editing; however, for better application in clinical settings, additional investigation is warranted.

Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning

The advent of induced pluripotent stem cell (iPSC) technology, which allows to transform one cell type into another, holds the promise to produce therapeutic cells and organs on demand. Realization of this objective is contingent on the ability to demonstrate quality and safety of the cellular product for its intended use. Bottlenecks and backlogs to the clinical use of iPSCs have been fully outlined and a need has emerged for safer and standardized protocols to trigger cell reprogramming and functional differentiation. Amidst great challenges, in particular associated with lengthy culture time and laborious cell characterization, a demand for faster and more accurate methods for the validation of cell identity and function at different stages of the iPSC manufacturing process has risen. Artificial intelligence-based methods are proving helpful for these complex tasks and might revolutionize the way iPSCs are managed to create surrogate cells and organs. Here, we briefly review recent progress in artificial intelligence approaches for evaluation of iPSCs and their derivatives in experimental studies.

OCT4-mediated inflammation induces cell reprogramming at the origin of cardiac valve development and calcification

Cell plasticity plays a key role in embryos by maintaining the differentiation potential of progenitors. Whether postnatal somatic cells revert to an embryonic-like naïve state regaining plasticity and redifferentiate into a cell type leading to a disease remains intriguing. Using genetic lineage tracing and single-cell RNA sequencing, we reveal that Oct4 is induced by nuclear factor κB (NFκB) at embyronic day 9.5 in a subset of mouse endocardial cells originating from the anterior heart forming field at the onset of endocardial-to-mesenchymal transition. These cells acquired a chondro-osteogenic fate. OCT4 in adult valvular aortic cells leads to calcification of mouse and human valves. These calcifying cells originate from the Oct4 embryonic lineage. Genetic deletion of Pou5f1 (Pit-Oct-Unc, OCT4) in the endocardial cell lineage prevents aortic stenosis and calcification of ApoE^−/− mouse valve. We established previously unidentified self-cell reprogramming NFκB- and OCT4-mediated inflammatory pathway triggering a dose-dependent mechanism of valve calcification.

Recent progress of iPSC technology in cardiac diseases

It has been nearly 15 years since the discovery of human-induced pluripotent stem cells (iPSCs). During this time, differentiation methods to targeted cells have dramatically improved, and many types of cells in the human body can be currently generated at high efficiency. In the cardiovascular field, the ability to generate human cardiomyocytes in vitro with the same genetic background as patients has provided a great opportunity to investigate human cardiovascular diseases at the cellular level to clarify the molecular mechanisms underlying the diseases and discover potential therapeutics. Additionally, iPSC-derived cardiomyocytes have provided a powerful platform to study drug-induced cardiotoxicity and identify patients at high risk for the cardiotoxicity; thus, accelerating personalized precision medicine. Moreover, iPSC-derived cardiomyocytes can be sources for cardiac cell therapy. Here, we review these achievements and discuss potential improvements for the future application of iPSC technology in cardiovascular diseases.

Bioengineering Systems for Modulating Notch Signaling in Cardiovascular Development, Disease, and Regeneration

The Notch intercellular signaling pathways play significant roles in cardiovascular development, disease, and regeneration through modulating cardiovascular cell specification, proliferation, differentiation, and morphogenesis. The dysregulation of Notch signaling leads to malfunction and maldevelopment of the cardiovascular system. Currently, most findings on Notch signaling rely on animal models and a few clinical studies, which significantly bottleneck the understanding of Notch signaling-associated human cardiovascular development and disease. Recent advances in the bioengineering systems and human pluripotent stem cell-derived cardiovascular cells pave the way to decipher the role of Notch signaling in cardiovascular-related cells (endothelial cells, cardiomyocytes, smooth muscle cells, fibroblasts, and immune cells), and intercellular crosstalk in the physiological, pathological, and regenerative context of the complex human cardiovascular system. In this review, we first summarize the significant roles of Notch signaling in individual cardiac cell types. We then cover the bioengineering systems of microfluidics, hydrogel, spheroid, and 3D bioprinting, which are currently being used for modeling and studying Notch signaling in the cardiovascular system. At last, we provide insights into ancillary supports of bioengineering systems, varied types of cardiovascular cells, and advanced characterization approaches in further refining Notch signaling in cardiovascular development, disease, and regeneration.

From Pancreatic β-Cell Gene Networks to Novel Therapies for Type 1 Diabetes

Completion of the Human Genome Project enabled a novel systems- and network-level understanding of biology, but this remains to be applied for understanding the pathogenesis of type 1 diabetes (T1D). We propose that defining the key gene regulatory networks that drive β-cell dysfunction and death in T1D might enable the design of therapies that target the core disease mechanism, namely, the progressive loss of pancreatic β-cells. Indeed, many successful drugs do not directly target individual disease genes but, rather, modulate the consequences of defective steps, targeting proteins located one or two steps downstream. If we transpose this to the T1D situation, it makes sense to target the pathways that modulate the β-cell responses to the immune assault-in relation to signals that may stimulate the immune response (e.g., HLA class I and chemokine overexpression and/or neoantigen expression) or inhibit the invading immune cells (e.g., PDL1 and HLA-E expression)-instead of targeting only the immune system, as it is usually proposed. Here we discuss the importance of a focus on β-cells in T1D, lessons learned from other autoimmune diseases, the "alternative splicing connection," data mining, and drug repurposing to protect β-cells in T1D and then some of the initial candidates under testing for β-cell protection.

In vivo Pooled Screening: A Scalable Tool to Study the Complexity of Aging and Age-Related Disease

Biological aging, and the diseases of aging, occur in a complex in vivo environment, driven by multiple interacting processes. A convergence of recently developed technologies has enabled in vivo pooled screening: direct administration of a library of different perturbations to a living animal, with a subsequent readout that distinguishes the identity of each perturbation and its effect on individual cells within the animal. Such screens hold promise for efficiently applying functional genomics to aging processes in the full richness of the in vivo setting. In this review, we describe the technologies behind in vivo pooled screening, including a range of options for delivery, perturbation and readout methods, and outline their potential application to aging and age-related disease. We then suggest how in vivo pooled screening, together with emerging innovations in each of its technological underpinnings, could be extended to shed light on key open questions in aging biology, including the mechanisms and limits of epigenetic reprogramming and identifying cellular mediators of systemic signals in aging.

PI3K/AKT phosphorylation activates ERRα by upregulating PGC‑1α and PGC‑1β in gallbladder cancer

The nuclear estrogen‑related receptor‑α (ERRα) is an orphan receptor that has been identified as a transcriptional factor. Peroxisome proliferator‑activated receptor‑γ (PPARγ) coactivator‑1‑α (PGC‑1α) and PPARγ coactivator‑1‑β (PGC‑1β) act as the co‑activators of ERRα. Our previous study reported that activated ERRα promoted the invasion and proliferation of gallbladder cancer cells by promoting PI3K/AKT phosphorylation. Therefore, the aim of the current study was to investigate whether PI3K/AKT phosphorylation could enhance ERRα activity in a positive feedback loop. LY294002 and insulin‑like growth factor I (IGF‑I) were used to inhibit and promote PI3K/AKT phosphorylation, respectively. A 3X ERE‑TATA luciferase reporter was used to measure ERRα activity. The present study found that LY294002 inhibited PI3K/AKT phosphorylation, decreased the proliferation and invasion of NOZ cells and suppressed the activity of ERRα. Conversely, IGF‑I induced PI3K/AKT phosphorylation, promoted the proliferation and invasion of NOZ cells and enhanced the activity of ERRα. The protein expression levels of PGC‑1α and PGC‑1β were elevated and reduced by IGF‑I and LY294002, respectively. Moreover, knockdown of PGC‑1α and PGC‑1β antagonized ERRα activation, which was enhanced by PI3K/AKT phosphorylation. Taken together, the present study demonstrated that PI3K/AKT phosphorylation triggered ERRα by upregulating the expression levels of PGC‑1α and PGC‑1β in NOZ cells.

PI3K/AKT phosphorylation activates ERRα by upregulating PGC‑1α and PGC‑1β in gallbladder cancer

The nuclear estrogen‑related receptor‑α (ERRα) is an orphan receptor that has been identified as a transcriptional factor. Peroxisome proliferator‑activated receptor‑γ (PPARγ) coactivator‑1‑α (PGC‑1α) and PPARγ coactivator‑1‑β (PGC‑1β) act as the co‑activators of ERRα. Our previous study reported that activated ERRα promoted the invasion and proliferation of gallbladder cancer cells by promoting PI3K/AKT phosphorylation. Therefore, the aim of the current study was to investigate whether PI3K/AKT phosphorylation could enhance ERRα activity in a positive feedback loop. LY294002 and insulin‑like growth factor I (IGF‑I) were used to inhibit and promote PI3K/AKT phosphorylation, respectively. A 3X ERE‑TATA luciferase reporter was used to measure ERRα activity. The present study found that LY294002 inhibited PI3K/AKT phosphorylation, decreased the proliferation and invasion of NOZ cells and suppressed the activity of ERRα. Conversely, IGF‑I induced PI3K/AKT phosphorylation, promoted the proliferation and invasion of NOZ cells and enhanced the activity of ERRα. The protein expression levels of PGC‑1α and PGC‑1β were elevated and reduced by IGF‑I and LY294002, respectively. Moreover, knockdown of PGC‑1α and PGC‑1β antagonized ERRα activation, which was enhanced by PI3K/AKT phosphorylation. Taken together, the present study demonstrated that PI3K/AKT phosphorylation triggered ERRα by upregulating the expression levels of PGC‑1α and PGC‑1β in NOZ cells.

NOTCH Signaling in Aortic Valve Development and Calcific Aortic Valve Disease

NOTCH intercellular signaling mediates the communications between adjacent cells involved in multiple biological processes essential for tissue morphogenesis and homeostasis. The NOTCH1 mutations are the first identified human genetic variants that cause congenital bicuspid aortic valve (BAV) and calcific aortic valve disease (CAVD). Genetic variants affecting other genes in the NOTCH signaling pathway may also contribute to the development of BAV and the pathogenesis of CAVD. While CAVD occurs commonly in the elderly population with tri-leaflet aortic valve, patients with BAV have a high risk of developing CAVD at a young age. This observation indicates an important role of NOTCH signaling in the postnatal homeostasis of the aortic valve, in addition to its prenatal functions during aortic valve development. Over the last decade, animal studies, especially with the mouse models, have revealed detailed information in the developmental etiology of congenital aortic valve defects. In this review, we will discuss the molecular and cellular aspects of aortic valve development and examine the embryonic pathogenesis of BAV. We will focus our discussions on the NOTCH signaling during the endocardial-to-mesenchymal transformation (EMT) and the post-EMT remodeling of the aortic valve. We will further examine the involvement of the NOTCH mutations in the postnatal development of CAVD. We will emphasize the deleterious impact of the embryonic valve defects on the homeostatic mechanisms of the adult aortic valve for the purpose of identifying the potential therapeutic targets for disease intervention.

Human Pluripotent Stem-Cell-Derived Models as a Missing Link in Drug Discovery and Development

Human pluripotent stem cells (hPSCs), including human embryonic stem cells (hESCs) and human-induced pluripotent stem cells (hiPSCs), have the potential to accelerate the drug discovery and development process. In this review, by analyzing each stage of the drug discovery and development process, we identified the active role of hPSC-derived in vitro models in phenotypic screening, target-based screening, target validation, toxicology evaluation, precision medicine, clinical trial in a dish, and post-clinical studies. Patient-derived or genome-edited PSCs can generate valid in vitro models for dissecting disease mechanisms, discovering novel drug targets, screening drug candidates, and preclinically and post-clinically evaluating drug safety and efficacy. With the advances in modern biotechnologies and developmental biology, hPSC-derived in vitro models will hopefully improve the cost-effectiveness and the success rate of drug discovery and development.

Sexual Dimorphism of the Heart: Genetics, Epigenetics, and Development

The democratization of genomic technologies has revealed profound sex biases in expression patterns in every adult tissue, even in organs with no conspicuous differences, such as the heart. With the increasing awareness of the disparities in cardiac pathophysiology between males and females, there are exciting opportunities to explore how sex differences in the heart are established developmentally. Although sexual dimorphism is traditionally attributed to hormonal influence, expression and epigenetic sex biases observed in early cardiac development can only be accounted for by the difference in sex chromosome composition, i.e., XX in females and XY in males. In fact, genes linked to the X and Y chromosomes, many of which encode regulatory factors, are expressed in cardiac progenitor cells and at every subsequent developmental stage. The effect of the sex chromosome composition may explain why many congenital heart defects originating before gonad formation exhibit sex biases in presentation, mortality, and morbidity. Some transcriptional and epigenetic sex biases established soon after fertilization persist in cardiac lineages, suggesting that early epigenetic events are perpetuated beyond early embryogenesis. Importantly, when sex hormones begin to circulate, they encounter a cardiac genome that is already functionally distinct between the sexes. Although there is a wealth of knowledge on the effects of sex hormones on cardiac function, we propose that sex chromosome-linked genes and their downstream targets also contribute to the differences between male and female hearts. Moreover, identifying how hormones influence sex chromosome effects, whether antagonistically or synergistically, will enhance our understanding of how sex disparities are established. We also explore the possibility that sexual dimorphism of the developing heart predicts sex-specific responses to environmental signals and foreshadows sex-biased health-related outcomes after birth.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.

Human Pluripotent Stem Cell-Derived Cardiac Cells: Application in Disease Modeling, Cell Therapy, and Drug Discovery

Cardiac diseases are the leading cause of deaths worldwide; however, to date, there has been limited progress in the development of therapeutic options for these conditions. Animal models have been the most extensively studied methods to recapitulate a wide variety of cardiac diseases, but these models exhibit species-specific differences in physiology, metabolism and genetics, which lead to inaccurate and unpredictable drug safety and efficacy results, resulting in drug attrition. The development of human pluripotent stem cell (hPSC) technology in theory guarantees an unlimited source of human cardiac cells. These hPSC-derived cells are not only well suited for traditional two-dimensional (2-D) monoculture, but also applicable to more complex systems, such as three-dimensional (3-D) organoids, tissue engineering and heart on-a-chip. In this review, we discuss the application of hPSCs in heart disease modeling, cell therapy, and next-generation drug discovery. While the hPSC-related technologies still require optimization, their advances hold promise for revolutionizing cell-based therapies and drug discovery.

Digital-Health-Highlights 2020

Neue digitale Technologien bieten großes Potenzial, die Gesundheitsversorgung in den kommenden Jahrzehnten grundlegend zu verändern. Die „Digital Health“ umfasst dabei nahezu alle Bereiche im Gesundheitssystem und nimmt – nicht nur wegen der aktuellen COVID-19-Pandemie – erheblich an Bedeutung zu. In Zukunft sollen nicht nur bekannte Prozesse digitalisiert werden, sondern neue digitale Maßnahmen eine Beteiligung des Patienten gezielt fördern und ihn aktiv in Diagnose- sowie Behandlungsprozess einbinden. Der „e-Patient“ erhält damit in Zukunft durch Smart Devices und Direct-to-Consumer-Technologien einen erleichterten Zugang zu seinen eigenen Gesundheitsdaten, die bisher in Datensilos aufbewahrt und nur eingeschränkt an ihn weitergegeben werden. Fortschritte in der Sensortechnologie und bei sog. Wearables ermöglichen nicht nur ein kontinuierliches Monitoring und einen Beitrag zu diesen patientenzentrierten Gesundheitsdaten, sondern ermöglichen neue Diagnoseverfahren und Therapien außerhalb des Krankenhauses. Die Digitalisierung liefert also zahlreiche Ansätze für eine effizientere und kostengünstigere Krankenversorgung und Prävention.

Decoding Genetics of Congenital Heart Disease Using Patient-Derived Induced Pluripotent Stem Cells (iPSCs)

Congenital heart disease (CHD) is the most common cause of infant death associated with birth defects. Recent next-generation genome sequencing has uncovered novel genetic etiologies of CHD, from inherited and de novo variants to non-coding genetic variants. The next phase of understanding the genetic contributors of CHD will be the functional illustration and validation of this genome sequencing data in cellular and animal model systems. Human induced pluripotent stem cells (iPSCs) have opened up new horizons to investigate genetic mechanisms of CHD using clinically relevant and patient-specific cardiac cells such as cardiomyocytes, endothelial/endocardial cells, cardiac fibroblasts and vascular smooth muscle cells. Using cutting-edge CRISPR/Cas9 genome editing tools, a given genetic variant can be corrected in diseased iPSCs and introduced to healthy iPSCs to define the pathogenicity of the variant and molecular basis of CHD. In this review, we discuss the recent progress in genetics of CHD deciphered by large-scale genome sequencing and explore how genome-edited patient iPSCs are poised to decode the genetic etiologies of CHD by coupling with single-cell genomics and organoid technologies.