PySmooth: a Python tool for the removal and correction of genotyping errors.

In genetic mapping studies involving many individuals, genome-wide markers such as single nucleotide polymorphisms (SNPs) can be detected using different methods. However, it comes with some errors. Some SNPs associated with diseases can be in regions encoding long noncoding RNAs (lncRNAs). Therefore, identifying the errors in genotype file and correcting them is crucial for accurate genetic mapping studies. We develop a Python tool called PySmooth, that offers an easy-to-use command line interface for the removal and correction of genotyping errors. PySmooth uses the approach of a previous tool called SMOOTH with some modifications. It inputs a genotype file, detects errors and corrects them. PySmooth provides additional features such as imputing missing data, better user-friendly usage, generates summary and visualization files, has flexible parameters, and handles more genotype codes. AVAILABILITY AND IMPLEMENTATION: PySmooth is available at https://github.com/lncRNAAddict/PySmooth .

ChromNetMotif: a Python tool to extract chromatin-sate marked motifs in a chromatin interaction network.

Motivation: Analysis of network motifs is crucial to studying the robustness, stability, and functions of complex networks. Genome organization can be viewed as a biological network that consists of interactions between different chromatin regions. These interacting regions are also marked by epigenetic or chromatin states which can contribute to the overall organization of the chromatin and proper genome function. Therefore, it is crucial to integrate the chromatin states of the nodes when performing motif analysis in chromatin interaction networks. Even though there has been increasing production of chromatin interaction and genome-wide epigenetic modification data, there is a lack of publicly available tools to extract chromatin state-marked motifs from genome organization data. Results: We develop a Python tool, ChromNetMotif, offering an easy-to-use command line interface to extract chromatin-state-marked motifs from a chromatin interaction network. The tool can extract occurrences, frequencies, and statistical enrichment of the chromatin state-marked motifs. Visualization files are also generated which allow the user to interpret the motifs easily. ChromNetMotif also allows the user to leverage the features of a multicore processor environment to reduce computation time for larger networks. The output files generated can be used to perform further downstream analysis. ChromNetMotif aims to serve as an important tool to comprehend the interplay between epigenetics and genome organization. Availability and implementation: ChromNetMotif is available at https://github.com/lncRNAAddict/ChromNetworkMotif.

Drosophila genotypes can be predicted from their exploration locomotive trajectories using supervised machine learning.

This study employs supervised machine learning algorithms to test whether locomotive features during exploratory activity in open field arenas can serve as predictors for the genotype of fruit flies. Because of the nonlinearity in locomotive trajectories, traditional statistical methods that are used to compare exploratory activity between genotypes of fruit flies may not reveal all insights. 10-minute-long trajectories of four different genotypes of fruit flies in an open-field arena environment were captured. Turn angles and step size features extracted from the trajectories were used for training supervised learning models to predict the genotype of the fruit flies. Using the first five minute locomotive trajectories, an accuracy of 83% was achieved in differentiating wild-type flies from three other mutant genotypes. Using the final 5 min and the entire ten minute duration decreased the performance indicating that the most variations between the genotypes in their exploratory activity are exhibited in the first few minutes. Feature importance analysis revealed that turn angle is a better predictor than step size in predicting fruit fly genotype. Overall, this study demonstrates that features of trajectories can be used to predict the genotype of fruit flies through supervised machine learning methods.

Association between Triplex-Forming Sites of Cardiac Long Noncoding RNA GATA6-AS1 and Chromatin Organization.

This study explored the relationship between 3D genome organization and RNA-DNA triplex-forming sites of long noncoding RNAs (lncRNAs), a group of RNAs that do not code for proteins but are important factors regulating different aspects of genome activity. The triplex-forming sites of anti-sense cardiac lncRNA GATA6-AS1 derived from DBD-Capture-Seq were examined and compared to modular features of 3D genome organization called topologically associated domains (TADs) obtained from Hi-C data. It was found that GATA6-AS1 triplex-forming sites are positioned non-randomly in TADs and their boundaries. The triplex sites showed a preference for TAD boundaries over internal regions of TADs. Computational prediction analysis indicated that CTCF, the key protein involved in TAD specification, may interact with GATA6-AS1, and their binding sites correlate with each other. Examining locations of repeat elements in the genome suggests that the ability of lncRNA GATA6-AS1 to form triplex sites with many genomic locations may be achieved by the rapid expansion of different repeat elements. Some of the triplex-forming sites were found to be positioned in regions that undergo dynamic chromatin organization events such as loss/gain of TAD boundaries during cardiac differentiation. These observed associations suggest that lncRNA-DNA triplex formation may contribute to the specification of TADs in 3D genome organization.

H19X-encoded miR-322(424)/miR-503 regulates muscle mass by targeting translation initiation factors.

BACKGROUND: Skeletal muscle atrophy is a debilitating complication of many chronic diseases, disuse conditions, and ageing. Genome-wide gene expression analyses have identified that elevated levels of microRNAs encoded by the H19X locus are among the most significant changes in skeletal muscles in a wide scope of human cachectic conditions. We have previously reported that the H19X locus is important for the establishment of striated muscle fate during embryogenesis. However, the role of H19X-encoded microRNAs in regulating skeletal mass in adults is unknown. METHODS: We have created a transgenic mouse strain in which ectopic expression of miR-322/miR-503 is driven by the skeletal muscle-specific muscle creatine kinase promoter. We also used an H19X mutant mouse strain in which transcription from the locus is interrupted by a gene trap. Animal phenotypes were analysed by standard histological methods. Underlying mechanisms were explored by using transcriptome profiling and validated in the two animal models and cultured myotubes. RESULTS: Our results demonstrate that the levels of H19X microRNAs are inversely related to postnatal skeletal muscle growth. Targeted overexpression of miR-322/miR-503 impeded skeletal muscle growth. The weight of gastrocnemius muscles of transgenic mice was only 54.5% of the counterparts of wild-type littermates. By contrast, interruption of transcription from the H19X locus stimulates postnatal muscle growth by 14.4-14.9% and attenuates the loss of skeletal muscle mass in response to starvation by 12.8-21.0%. Impeded muscle growth was not caused by impaired IGF1/AKT/mTOR signalling or a hyperactive ubiquitin-proteasome system, instead accompanied by markedly dropped abundance of translation initiation factors in transgenic mice. miR-322/miR-503 directly targets eIF4E, eIF4G1, eIF4B, eIF2B5, and eIF3M. CONCLUSIONS: Our study illustrates a novel pathway wherein H19X microRNAs regulate skeletal muscle growth and atrophy through regulating the abundance of translation initiation factors, thereby protein synthesis. The study highlights how translation initiation factors lie at the crux of multiple signalling pathways that control skeletal muscle mass.

LncRNA:DNA triplex-forming sites are positioned at specific areas of genome organization and are predictors for Topologically Associated Domains.

BACKGROUND: Chromosomes are organized into units called topologically associated domains (TADs). TADs dictate regulatory landscapes and other DNA-dependent processes. Even though various factors that contribute to the specification of TADs have been proposed, the mechanism is not fully understood. Understanding the process for specification and maintenance of these units is essential in dissecting cellular processes and disease mechanisms. RESULTS: In this study, we report a genome-wide study that considers the idea of long noncoding RNAs (lncRNAs) mediating chromatin organization using lncRNA:DNA triplex-forming sites (TFSs). By analyzing the TFSs of expressed lncRNAs in multiple cell lines, we find that they are enriched in TADs, their boundaries, and loop anchors. However, they are evenly distributed across different regions of a TAD showing no preference for any specific portions within TADs. No relationship is observed between the locations of these TFSs and CTCF binding sites. However, TFSs are located not just in promoter regions but also in intronic, intergenic, and 3'UTR regions. We also show these triplex-forming sites can be used as predictors in machine learning models to discriminate TADs from other genomic regions. Finally, we compile a list of important "TAD-lncRNAs" which are top predictors for TADs identification. CONCLUSIONS: Our observations advocate the idea that lncRNA:DNA TFSs are positioned at specific areas of the genome organization and are important predictors for TADs. LncRNA:DNA triplex formation most likely is a general mechanism of action exhibited by some lncRNAs, not just for direct gene regulation but also to mediate 3D chromatin organization.

Aberrant expression of embryonic mesendoderm factor MESP1 promotes tumorigenesis.

BACKGROUND: Mesoderm Posterior 1 (MESP1) belongs to the family of basic helix-loop-helix transcription factors. It is a master regulator of mesendoderm development, leading to formation of organs such as heart and lung. However, its role in adult pathophysiology remains unknown. Here, we report for the first time a previously-unknown association of MESP1 with non-small cell lung cancer (NSCLC). METHODS: MESP1 mRNA and protein levels were measured in NSCLC-derived cells by qPCR and immunoblotting respectively. Colony formation assay, colorimetric cell proliferation assay and soft agar colony formation assays were used to assess the effects of MESP1 knockdown and overexpression in vitro. RNA-sequencing and chromatin immunoprecipitation (ChIP)-qPCR were used to determine direct target genes of MESP1. Subcutaneous injection of MESP1-depleted NSCLC cells in immuno-compromised mice was done to study the effects of MESP1 mediated tumor formation in vivo. FINDINGS: We found that MESP1 expression correlates with poor prognosis in NSCLC patients, and is critical for proliferation and survival of NSCLC-derived cells, thus implicating MESP1 as a lung cancer oncogene. Ectopic MESP1 expression cooperates with loss of tumor suppressor ARF to transform murine fibroblasts. Xenografts from MESP1-depleted cells showed decreased tumor growth in vivo. Global transcriptome analysis revealed a MESP1 DNA-binding-dependent gene signature associated with various hallmarks of cancer, suggesting that transcription activity of MESP1 is most likely responsible for its oncogenic abilities. INTERPRETATION: Our study demonstrates MESP1 as a previously-unknown lineage-survival oncogene in NSCLC which may serve as a potential prognostic marker and therapeutic target for lung cancer in the future.

Accurate prediction of boundaries of high resolution topologically associated domains (TADs) in fruit flies using deep learning.

Genomes are organized into self-interacting chromatin regions called topologically associated domains (TADs). A significant number of TAD boundaries are shared across multiple cell types and conserved across species. Disruption of TAD boundaries may affect the expression of nearby genes and could lead to several diseases. Even though detection of TAD boundaries is important and useful, there are experimental challenges in obtaining high resolution TAD locations. Here, we present computational prediction of TAD boundaries from high resolution Hi-C data in fruit flies. By extensive exploration and testing of several deep learning model architectures with hyperparameter optimization, we show that a unique deep learning model consisting of three convolution layers followed by a long short-term-memory layer achieves an accuracy of 96%. This outperforms feature-based models' accuracy of 91% and an existing method's accuracy of 73-78% based on motif TRAP scores. Our method also detects previously reported motifs such as Beaf-32 that are enriched in TAD boundaries in fruit flies and also several unreported motifs.

Transient activation of AMPK preceding left ventricular pressure overload reduces adverse remodeling and preserves left ventricular function.

Coordinated changes in signaling pathways and gene expression in hearts subjected to prolonged stress maintain cardiac function. Loss of steroid receptor coactivator-2 (SRC-2) results in a reversal to the fetal gene program and disrupts the response to pressure overload, accompanied by prominent effects on metabolism and growth signaling, including increased AMPK activation. We proposed that early metabolic stress driven by AMPK activation induces contractile dysfunction in mice lacking SRC-2. We used 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) to activate AMPK transiently before transverse aortic constriction (TAC) in wild-type and cardiomyocyte-specific SRC-2 knockout (CKO) animals. In contrast to AMPK activities during stress, in unstressed hearts, AICAR induced a mild activation of Akt signaling, and, in SRC-2-CKO mice, partially relieved an NAD+ deficiency and increased antioxidant signaling. These molecular changes translated to a mild hypertrophic response to TAC with decreased maladaptive remodeling, including markedly decreased fibrosis. Additionally, preactivation of AMPK in SRC-2-CKO mice was accompanied by a dramatic improvement in cardiac function compared with saline-treated SRC-2-CKO mice. Our results show that altered molecular signaling before stress onset has extended effects on sustained cardiac stress responses, and prestress modulation of transient growth and metabolism pathways may control those effects.-Nam, D. H., Kim, E., Benham, A., Park, H.-K., Soibam, B., Taffet, G. E., Kaelber, J. T., Suh, J. H., Taegtmeyer, H., Entman, M. L., Reineke, E. L. Transient activation of AMPK preceding left ventricular pressure overload reduces adverse remodeling and preserves left ventricular function.

Coregulatory long non-coding RNA and protein-coding genes in serum starved cells.

Serum starvation is widely used in cell biology to trigger cell cycle arrest, apoptosis, autophagy, and metabolic adaptations. Serum starvation-related molecular events have been well characterized at protein level but not at transcript level: how long non-coding RNAs contribute to the regulation of protein-coding genes is largely unknown. Here, we captured the lncRNA transcriptome in serum starved mouse embryonic fibroblasts and identified three main modes of action: cis-acting/coregulatory, trans-acting, and "miRNA-carrier". Whole-genome and individual gene level analyses support that our annotation provides an important platform for understanding lncRNA/protein-coding gene coregulatory mechanisms in serum starvation.

H19X-encoded miR-424(322)/-503 cluster: emerging roles in cell differentiation, proliferation, plasticity and metabolism.

miR-424(322)/-503 are mammal-specific members of the extended miR-15/107 microRNA family. They form a co-expression network with the imprinted lncRNA H19 in tetrapods. miR-424(322)/-503 regulate fundamental cellular processes including cell cycle, epithelial-to-mesenchymal transition, hypoxia and other stress response. They control tissue differentiation (cardiomyocyte, skeletal muscle, monocyte) and remodeling (mammary gland involution), and paradoxically participate in tumor initiation and progression. Expression of miR-424(322)/-503 is governed by unique mechanisms involving sex hormones. Here, we summarize current literature and provide a primer for future endeavors.

Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs.

Long noncoding RNAs (lncRNAs) can exert their function by interacting with the DNA via triplex structure formation. Even though this has been validated with a handful of experiments, a genome-wide analysis of lncRNA-DNA binding is needed. In this paper, we develop and interpret deep learning models that predict the genome-wide binding sites deciphered by ChIRP-Seq experiments of 12 different lncRNAs. Among the several deep learning architectures tested, a simple architecture consisting of two convolutional neural network layers performed the best suggesting local sequence patterns as determinants of the interaction. Further interpretation of the kernels in the model revealed that these local sequence patterns form triplex structures with the corresponding lncRNAs. We uncovered several novel triplexes forming domains (TFDs) of these 12 lncRNAs and previously experimentally verified TFDs of lncRNAs HOTAIR and MEG3. We experimentally verified such two novel TFDs of lncRNAs HOTAIR and TUG1 predicted by our method (but previously unreported) using Electrophoretic mobility shift assays. In conclusion, we show that simple deep learning architecture can accurately predict genome-wide binding sites of lncRNAs and interpretation of the models suggest RNA:DNA:DNA triplex formation as a viable mechanism underlying lncRNA-DNA interactions at genome-wide level.

ERß alters the chemosensitivity of luminal breast cancer cells by regulating p53 function.

Estrogen receptor α (ERα)-positive breast cancers tend to develop resistance to both endocrine therapy and chemotherapy. Despite recent progress in defining molecular pathways that confer endocrine resistance, the mechanisms that regulate chemotherapy response in luminal tumors remain largely elusive. Luminal tumors often express wild-type p53 that is a major determinant of the cellular DNA damage response. Similar to p53, the second ER subtype, ERß, has been reported to inhibit breast tumorigenesis by acting alone or in collaboration with p53. However, a synergistic mechanism of action has not been described. Here, we suggest that ERß relies on p53 to elicit its tumor repressive actions in ERα-positive breast cancer cells. Upregulation of ERß and treatment with ERß agonists potentiates the tumor suppressor function of p53 resulting in decreased survival. This effect requires molecular interaction between the two proteins that disrupts the inhibitory action of ERα on p53 leading to increased transcriptional activity of p53. In addition, we show that the same interaction alters the chemosensitivity of endocrine-resistant cells including their response to tamoxifen therapy. Our results suggest a collaboration of ERß and p53 tumor suppressor activity in breast cancer cells that indicates the importance of ligand-regulated ERß as a tool to target p53 activity and improve the clinical management of resistant disease.

Predictors of breast cancer cell types and their prognostic power in breast cancer patients.

BACKGROUND: Comprehensive understanding of intratumor heterogeneity requires identification of molecular markers, which are capable of differentiating different subpopulations and which also have clinical significance. One important tool that has been addressing this issue is single cell RNA-Sequencing (scRNASeq) that allows the quantification of expression profiles of transcripts in individual cells in a population of cancer cells. Using the expression profiles from scRNASeq, current studies conduct analysis to group cells into different subpopulations using clustering algorithms. In this study, we explore scRNASeq cancer data from a different perspective. We focus on scRNASeq data originating from cancer cells pertaining to a particular cancer type, where the cell type or the subpopulation to which each cell belongs is known. We investigate if the "cell type" of a cancer cell can be predicted based on the expression profiles of a small set of transcripts. RESULTS: We outline a predictive analytics pipeline to accurately predict 6 breast cancer cell types using single cell gene expression profiles. Instead of building predictive models using the complete human transcripts, the pipeline first eliminates predictors with low expression and low variance. A multinomial penalized logistic regression further reduces the size of the predictors to only 308, out of which 34 are long non-coding RNAs. Tuning of predictive models shows support vector machines and neural networks as the most accurate models achieving close to 98% prediction accuracies. We also find that mixture of protein coding genes and long non-coding RNAs are better predictors compared to when the two sets of transcripts are treated separately. A signature risk score originating from 65 protein coding genes and 5 lncRNA predictors is associated with prognostic survival of TCGA breast cancer patients. This association was maintained when the risk scores were generated using 65 PCGs and 5 lncRNA separately. We further show that predictors restricted to a particular cell type serve as better prognostic markers for the respective patient subtype. CONCLUSION: Our results show that in general, the breast cancer cell type predictors are also associated with patient survivability and hence have clinical significance.

Recellularization of rat liver: An in vitro model for assessing human drug metabolism and liver biology.

Liver-like organoids that recapitulate the complex functions of the whole liver by combining cells, scaffolds, and mechanical or chemical cues are becoming important models for studying liver biology and drug metabolism. The advantages of growing cells in three-dimensional constructs include enhanced cell-cell and cell-extracellular matrix interactions and preserved cellular phenotype including, prevention of de-differentiation. In the current study, biomimetic liver constructs were made via perfusion decellularization of rat liver, with the goal of maintaining the native composition and structure of the extracellular matrix. We optimized our decellularization process to produce liver scaffolds in which immunogenic residual DNA was removed but glycosaminoglycans were maintained. When the constructs were recellularized with rat or human liver cells, the cells remained viable, capable of proliferation, and functional for 28 days. Specifically, the cells continued to express cytochrome P450 genes and maintained their ability to metabolize a model drug, midazolam. Microarray analysis showed an upregulation of genes involved in liver regeneration and fibrosis. In conclusion, these liver constructs have the potential to be used as test beds for studying liver biology and drug metabolism.

Super-lncRNAs: identification of lncRNAs that target super-enhancers via RNA:DNA:DNA triplex formation.

Super-enhancers are characterized by high levels of Mediator binding and are major contributors to the expression of their associated genes. They exhibit high levels of local chromatin interactions and a higher order of local chromatin organization. On the other hand, lncRNAs can localize to specific DNA sites by forming a RNA:DNA:DNA triplex, which in turn can contribute to local chromatin organization. In this paper, we characterize a new class of lncRNAs called super-lncRNAs that target super-enhancers and which can contribute to the local chromatin organization of the super-enhancers. Using a logistic regression model based on the number of RNA:DNA:DNA triplex sites a lncRNA forms within the super-enhancer, we identify 442 unique super-lncRNA transcripts in 27 different human cell and tissue types; 70% of these super-lncRNAs were tissue restricted. They primarily harbor a single triplex-forming repeat domain, which forms an RNA:DNA:DNA triplex with multiple anchor DNA sites (originating from transposable elements) within the super-enhancers. Super-lncRNAs can be grouped into 17 different clusters based on the tissue or cell lines they target. Super-lncRNAs in a particular cluster share common short structural motifs and their corresponding super-enhancer targets are associated with gene ontology terms pertaining to the tissue or cell line. Super-lncRNAs may use these structural motifs to recruit and transport necessary regulators (such as transcription factors and Mediator complexes) to super-enhancers, influence chromatin organization, and act as spatial amplifiers for key tissue-specific genes associated with super-enhancers.

HIRA deficiency in muscle fibers causes hypertrophy and susceptibility to oxidative stress.

Nucleosome assembly proceeds through DNA replication-coupled or replication-independent mechanisms. For skeletal myocytes, whose nuclei have permanently exited the cell cycle, replication-independent assembly is the only mode available for chromatin remodeling. For this reason, any nucleosome composition alterations accompanying transcriptional responses to physiological signals must occur through a DNA replication-independent pathway. HIRA is the histone chaperone primarily responsible for replication-independent incorporation of histone variant H3.3 across gene bodies and regulatory regions. Thus, HIRA would be expected to play an important role in epigenetically regulating myocyte gene expression. The objective of this study was to determine the consequence of eliminating HIRA from mouse skeletal myocytes. At 6 weeks of age, myofibers lacking HIRA showed no pathological abnormalities; however, genes involved in transcriptional regulation were downregulated. By 6 months of age, myofibers lacking HIRA exhibited hypertrophy, sarcolemmal perforation and oxidative damage. Genes involved in muscle growth and development were upregulated, but those associated with responses to cellular stresses were downregulated. These data suggest that elimination of HIRA produces a hypertrophic response in skeletal muscle and leaves myofibers susceptible to stress-induced degeneration.

MicroRNAs and ectodermal specification I. Identification of miRs and miR-targeted mRNAs in early anterior neural and epidermal ectoderm.

The establishment of cell lineages occurs via a dynamic progression of gene regulatory networks (GRNs) that underlie developmental commitment and differentiation. To investigate how microRNAs (miRs) function in this process, we compared miRs and miR targets at the initiation of the two major ectodermal lineages in Xenopus. We used next-generation sequencing to identify over 170 miRs expressed in midgastrula ectoderm expressing either noggin or a constitutively active BMP receptor, reflecting anterior neural or epidermal ectoderm, respectively; 125 had not previously been identified in Xenopus. We identified the locations of the pre-miR sequences in the X. laevis genome. Neural and epidermal ectoderm express broadly similar sets of miRs. To identify targets of miR-dependent translational control, we co-immunoprecipitated Argonaute-Ribonucleoprotein (Ago-RNP) complexes from early neural and epidermal ectoderm and sequenced the associated RNA. The Ago-RNP RNAs from these tissues represent overlapping, yet distinct, subsets of genes. Moreover, the profile of Ago-RNP associated genes differs substantially from the profile of total RNAs in these tissues. We generated target predictions for the "high confidence" Ago-RNP RNAs using the identified ectodermal miRs; These RNAs generally had target sites for multiple miRs. Oct4 orthologues, as well as many of their previously identified transcriptional targets, are represented in the Ago-RNP pool in both tissues, suggesting that miR-dependent regulation contributes to the downregulation of the oct4 gene regulatory network and the reduction in ectodermal pluripotency.

Data on microRNAs and microRNA-targeted mRNAs in Xenopus ectoderm.

Small RNAs from early neural (i.e., Noggin-expressing, or NOG) and epidermal (expressing a constitutively active BMP4 receptor, CABR) ectoderm in Xenopus laevis were sequenced to identify microRNAs (miRs) expressed in each tissue. Argonaute-associated mRNAs were isolated and sequenced to identify genes that are regulated by microRNAs in these tissues. Interactions between these ectodermal miRs and selected miR-regulated mRNAs were predicted using the PITA algorithm; PITA predictions for over 600 mRNAs are presented. All sequencing data are available at NCBI (NCBI Bioproject Accession number: PRJNA325834). This article accompanies the manuscript "MicroRNAs and ectodermal specification I. Identification of miRs and miR-targeted mRNAs in early anterior neural and epidermal ectoderm" (V.V. Shah, B. Soibam, R.A. Ritter, A. Benham, J. Oomen, A.K. Sater, 2016) [1].

Mesp1 Marked Cardiac Progenitor Cells Repair Infarcted Mouse Hearts.

Mesp1 directs multipotential cardiovascular cell fates, even though it's transiently induced prior to the appearance of the cardiac progenitor program. Tracing Mesp1-expressing cells and their progeny allows isolation and characterization of the earliest cardiovascular progenitor cells. Studying the biology of Mesp1-CPCs in cell culture and ischemic disease models is an important initial step toward using them for heart disease treatment. Because of Mesp1's transitory nature, Mesp1-CPC lineages were traced by following EYFP expression in murine Mesp1(Cre/+); Rosa26(EYFP/+) ES cells. We captured EYFP+ cells that strongly expressed cardiac mesoderm markers and cardiac transcription factors, but not pluripotent or nascent mesoderm markers. BMP2/4 treatment led to the expansion of EYFP+ cells, while Wnt3a and Activin were marginally effective. BMP2/4 exposure readily led EYFP+ cells to endothelial and smooth muscle cells, but inhibition of the canonical Wnt signaling was required to enter the cardiomyocyte fate. Injected mouse pre-contractile Mesp1-EYFP+ CPCs improved the survivability of injured mice and restored the functional performance of infarcted hearts for at least 3 months. Mesp1-EYFP+ cells are bona fide CPCs and they integrated well in infarcted hearts and emerged de novo into terminally differentiated cardiac myocytes, smooth muscle and vascular endothelial cells.