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A retinoic acid:YAP1 signaling axis controls atrial lineage commitment

Cell Reports 2025年第5期44卷 115687页

作者： Abraham, Elizabeth Kostina, Aleksandra Volmert, Brett Roule, Thomas Huang, Ling Yu, Jingting Williams, April E. Megill, Emily Douglas, Aidan Pericak, Olivia M. Morris, Alex Stronati, Eleonora Larrinaga-Zamanillo, Arantza Fueyo, Raquel Zubillaga, Mikel Andrake, Mark D. Akizu, Naiara Aguirre, Aitor Estaras, Conchi Department of Cardiovascular Sciences Aging + Cardiovascular Discovery Center Lewis Katz School of Medicine Temple University Philadelphia 19140 PA United States Institute for Quantitative Health Science and Engineering Division of Developmental and Stem Cell Biology Michigan State University East Lansing 48824 MI United States Department of Biomedical Engineering College of Engineering Michigan State University East Lansing 48824 MI United States Raymond G. Perelman Center for Cellular and Molecular Therapeutics The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Child and Adolescence Psychiatry The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Department of Chemical and Systems Biology Stanford University School of Medicine Stanford 94305 CA United States Cancer Epigenetics Institute Fox Chase Cancer Center Philadelphia 19111 PA United States Molecular Modeling Facility Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia 19111 PA United States

In cardiac progenitor cells (CPCs), retinoic acid (RA) signaling induces atrial lineage gene expression and acquisition of an atrial cell fate. To achieve this, RA coordinates a complex regulatory network of downstream effectors that is not fully identified. To address this gap, we applied a functional genomics approach (i.e., scRNA-seq and snATAC-seq) to untreated and RA-treated human embryonic stem cell (hESC)-derived CPCs. Unbiased analysis revealed that the Hippo effectors YAP1 and TEAD4 are integrated with the atrial transcription factor enhancer network and that YAP1 activates RA enhancers in CPCs. Furthermore, Yap1 deletion in mouse embryos compromises the expression of RA-induced genes, such as Nr2f2, in the CPCs of the second heart field. Accordingly, in hESC-derived patterned heart organoids, YAP1 regulates the formation of an atrial chamber but is dispensable for the formation of a ventricle. Overall, our findings revealed that YAP1 cooperates with RA signaling to induce atrial lineages during cardiogenesis. © 2025 The Author(s)

关键词： atrial differentiation cardiac enhancers cardiac progenitors chamber development CP: Developmental biology heart organoids NR2F2 retinoic acid signaling SHF TEAD4 YAP1

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Comprehensive characterization of the Published Kinase Inhibitor Set

Comprehensive characterization of the Published Kinase Inhib...

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作者： Elkins, Jonathan M. Fedele, Vita Szklarz, Marta Abdul Azeez, Kamal R. Salah, Eidarus Mikolajczyk, Jowita Romanov, Sergei Sepetov, Nikolai Huang, Xi-Ping Roth, Bryan L. Al Haj Zen, Ayman Fourches, Denis Muratov, Eugene Tropsha, Alex Morris, Joel Teicher, Beverly A. Kunkel, Mark Polley, Eric Lackey, Karen E. Atkinson, Francis L. Overington, John P. Bamborough, Paul Müller, Susanne Price, Daniel J. Willson, Timothy M. Drewry, David H. Knapp, Stefan Zuercher, William J. Structural Genomics Consortium and Target Discovery Institute Nuffield Department of Clinical Medicine Old Road Campus University of Oxford Oxford United Kingdom Nanosyn Inc. Santa ClaraCA United States Department of Pharmacology Division of Chemical Biology and Medicinal Chemistry University of North Carolina Chapel Hill School of Medicine Chapel HillNC United States British Heart Foundation Centre of Research Excellence Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford Oxford United Kingdom Laboratory for Molecular Modeling Division of Chemical Biology and Medicinal Chemistry UNC Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel HillNC United States Division of Cancer Treatment and Diagnosis National Cancer Institute RockvilleMD United States Medical University of South Carolina CharlestonSC United States Wellcome Trust Genome Campus Hinxton Cambridge United Kingdom Chemical Sciences GlaxoSmithKline Stevenage United Kingdom Chemical Sciences GlaxoSmithKline Research Triangle ParkNC United States Institute for Pharmaceutical Chemistry Johann Wolfgang Goethe-University Frankfurt am Main Germany Frankfurt am Main Germany Novartis Vaccines and Diagnostics Holly SpringsNC United States Department of Chemistry Bioinformatics Research Center North Carolina State University RaleighNC United States Stratified Medical London United Kingdom Meryx Pharmaceuticals Chapel HillNC United States SGC-UNC Division of Chemical Biology and Medicinal Chemistry UNC Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel HillNC United States

Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein-coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome. © 2016 Nature America, Inc. All rights reserved.

关键词： Cell proliferation

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An objective approach towards assessment of the physiological age of heart

An objective approach towards assessment of the physiologica...

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2008 AAAI Fall Symposium

作者： Tavakolian, Kouhyar Akhbardeh, Alireza Kaminska, Bozena School of Engineering Science Simon Fraser University 8888 University Drive Burnaby BC V5A 1S6 Canada Center for Cardiovascular Modeling and Bioinformatics Whiting School of Engineering Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)9781577353942

A novel objective methodology is proposed for the assessment of the physiological age of the heart which is based on extraction of wavelet features from sternal ballistocardiogram and classification of these features using neural networks and k-nearest neighbor classifiers. Some promising preliminary results suggest that the proposed methodology is feasible to be developed to a tool for assessment of the physiological age of the heart independent of its chronological age and present a new tool for the assessment of the cardiac performance.

关键词： Heart

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Diffeomorphic matching of diffusion tensor images

Diffeomorphic matching of diffusion tensor images

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2006 Conference on Computer Vision and Pattern Recognition Workshops

作者： Cao, Yan Miller, Michael I. Mori, Susumu Winslow, Raimond L. Younes, Laurent Center for Imaging Science Johns Hopkins University Department of Radiology Johns Hopkins University School of Medicine Center for Cardiovascular Bioinformatics and Modeling Whitaker Biomedical Engineering Institute Johns Hopkins University

ISBN: (纸本)0769526462

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multikernel-width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images. © 2006 IEEE.

关键词： Pattern matching

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Constructing gene expression-based diagnostic rules for understanding individualized etiology of heart failure

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Journal of Cardiothoracic-Renal Research 2006年第1期1卷 33-40页

作者： Gao, Zhong Tomaselli, Gordon Wei, Chiming Winslow, Raimond Center for Cardiovascular Bioinformatics and Modeling School of Medicine Johns Hopkins University Baltimore MD 21218 3400 North Charles Street United States Division of Cardiology School of Medicine Johns Hopkins University Baltimore MD 21205 United States Department of Surgery School of Medicine Johns Hopkins University Baltimore MD 21205 United States

Gene expression profiling has the potential to improve individualized etiology diagnosis. A statistical approach based on a multidimensional scaling (MDS) vector model is introduced to construct patient-specific rules for individualized diagnosis based on gene expression profiles. The method has a dual function of discovering new disease classes/ subclasses as well as constructing patient-specific diagnostic rules without prior knowledge of class distinction. The diagnostic rule consists of two components: (1) diagnostic gene expression pattern that suggests a critical etiological condition associated with a disease category, and (2) patient-specific correlations to the diagnostic pattern. The method is applied to construct the diagnostic rule for heart failure by which the heart failure etiology has been successfully discerned with gene expression profiles. The diagnostic rule for two potential heart failure sub-classes has been constructed to further classify heart failure patients and exploit related molecular pathogenesis. Furthermore, the diagnostic gene expression patterns reveal molecular mechanisms relevant to heart failure, and facilitate biomarker identification. The method provides an approach to exploring feasibility of gene expression profiling in individualized etiology diagnosis for therapeutic decision-making. © 2006 Asian-Pacific Cardiothoracic-Renal Association (APCRA). Published by Elsevier Ltd. All rights reserved.

关键词： Diagnostic rules Etiology Gene expression Heart failure

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Diffeomorphic Matching of Diffusion Tensor Images

Diffeomorphic Matching of Diffusion Tensor Images

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yan Cao M.I. Miller S. Mori R.L. Winslow L. Younes Center for Imaging Science Johns Hopkins University USA Department of Radiology and Kennedy Krieger Institute School of Medicine Johns Hopkins University USA Center for Cardiovascular Bioinformatics & Modeling and the Whitaker Biomedical Engineering Institute Johns Hopkins University USA

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multikernel- width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images.

关键词： Tensile stress Diffusion tensor imaging Biomedical imaging Optimization methods Magnetic field measurement Extraterrestrial measurements Deformable models Magnetic resonance Joining processes Geophysics computing

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Large deformation diffeomorphic metric mapping of fiber orientations

Large deformation diffeomorphic metric mapping of fiber orie...

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Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005

作者： Cao, Yan Miller, Michael I. Winslow, Raimond L. Younes, Laurent Center for Imaging Science Johns Hopkins University Baltimore MD 21218 Center for Cardiovascular Bioinformatics and Modeling Whitaker Biomedical Engineering Institute Johns Hopkins University Baltimore MD 21218

ISBN: (纸本)076952334X

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type ofdataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images. © 2005 IEEE.

关键词： Fiber reinforced materials

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Large deformation diffeomorphic metric mapping of fiber orientations

Large deformation diffeomorphic metric mapping of fiber orie...

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International Conference on Computer Vision (ICCV)

作者： Yan Cao M.I. Miller R.L. Winslow L. Younes Center for Imaging Science Johns Hopkins University Baltimore MD USA Center for Cardiovascular Bioinformatics & Modeling and Whitaker Biomedical Engineering Institute Johns Hopkins University Baltimore MD USA

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the large deformation diffeomorphic metric-mapping framework to this type of dataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images

关键词： Diffusion tensor imaging Tensile stress Heart Cardiac disease Magnetic resonance imaging Image registration Biomedical imaging Biological tissues cardiovascular diseases Cardiology

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