Ontology highlight
ABSTRACT:
PROVIDER: PRJNA345231 | ENA |
REPOSITORIES: ENA
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Project description:Heart failure is driven by the interplay between master regulatory transcription factors and dynamic alterations in chromatin structure. Coordinate activation of developmental, inflammatory, fibrotic and growth regulators underlies the hallmark phenotypes of pathologic cardiac hypertrophy and contractile failure. While transactivation in this context is known to be associated with recruitment of histone acetyl-transferase enzymes and local chromatin hyperacetylation, the role of epigenetic reader proteins in cardiac biology is unknown. We therefore undertook a first study of acetyl-lysine reader proteins, or bromodomains, in heart failure. Using a chemical genetic approach, we establish a central role for BET-family bromodomain proteins in gene control during the evolution of heart failure. BET inhibition suppresses cardiomyocyte hypertrophy in a cell-autonomous manner, confirmed by RNA interference in vitro. Following both pressure overload and neurohormonal stimulation, BET inhibition potently attenuates pathologic cardiac remodeling in vivo. Integrative transcriptional and epigenomic analyses reveal that BET proteins function mechanistically as pause-release factors critical to activation of canonical master regulators and effectors that are central to heart failure pathogenesis. Specifically, BET bromodomain inhibition in mice abrogates pathology-associated pause release and transcriptional elongation, thereby preventing activation of cardiac transcriptional pathways relevant to the gene expression profile of failing human hearts. This study implicates epigenetic readers in cardiac biology and identifies BET co-activator proteins as therapeutic targets in heart failure. ChIP-Seq of mouse heart tissues from mice induced with heart failure and treated with JQ1 BET bromodomain inhibitor
2013-08-06 | E-GEOD-46668 | biostudies-arrayexpress
Project description:Heart failure is driven by the interplay between master regulatory transcription factors and dynamic alterations in chromatin structure. Coordinate activation of developmental, inflammatory, fibrotic and growth regulators underlies the hallmark phenotypes of pathologic cardiac hypertrophy and contractile failure. While transactivation in this context is known to be associated with recruitment of histone acetyl-transferase enzymes and local chromatin hyperacetylation, the role of epigenetic reader proteins in cardiac biology is unknown. We therefore undertook a first study of acetyl-lysine reader proteins, or bromodomains, in heart failure. Using a chemical genetic approach, we establish a central role for BET-family bromodomain proteins in gene control during the evolution of heart failure. BET inhibition suppresses cardiomyocyte hypertrophy in a cell-autonomous manner, confirmed by RNA interference in vitro. Following both pressure overload and neurohormonal stimulation, BET inhibition potently attenuates pathologic cardiac remodeling in vivo. Integrative transcriptional and epigenomic analyses reveal that BET proteins function mechanistically as pause-release factors critical to activation of canonical master regulators and effectors that are central to heart failure pathogenesis. Specifically, BET bromodomain inhibition in mice abrogates pathology-associated pause release and transcriptional elongation, thereby preventing activation of cardiac transcriptional pathways relevant to the gene expression profile of failing human hearts. This study implicates epigenetic readers in cardiac biology and identifies BET co-activator proteins as therapeutic targets in heart failure.
2013-08-06 | GSE46668 | GEO
Project description:Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these tools remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Thus, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesized and characterized in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperformed (R2=0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2=0.04-0.15) and allowed for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This deep learning approach constitutes a major step forward in engineering and understanding of RNA synthetic biology.
2020-08-12 | GSE149225 | GEO
Project description:Populations of engineered metabolite-producing microorganisms are prone to evolutionary production declines during industrial-scale cultivations. In this study, we develop a synthetic product addiction system in E coli that addicts mevalonic acid production cells to mevalonic acid. Through experimentally simuluated long-term fermentation, we investigate how product-addicted organisms remain stable and avoid formation of genetic subpopulations of fit, non-producing cells.
2018-02-09 | E-MTAB-6182 | biostudies-arrayexpress
Project description:Vineetha Mandlik, Mayuri Gurav & Shailza Singh. Regulatory dynamics of network architecture and function in tristable genetic circuit of Leishmania: a mathematical biology approach. Journal of Biomolecular Structure and Dynamics 33, 12 (2015).
The emerging field of synthetic biology has led to the design of tailor-made synthetic circuits for several therapeutic applications. Biological networks can be reprogramed by designing synthetic circuits that modulate the expression of target proteins. IPCS (inositol phosphorylceramide synthase) has been an attractive target in the sphingolipid metabolism of the parasite Leishmania. In this study, we have constructed a tristable circuit for the IPCS protein. The circuit has been validated and its long-term behavior has been assessed. The robustness and evolvability of the circuit has been estimated using evolutionary algorithms. The tristable synthetic circuit has been specifically designed to improve the rate of production of phosphatidylcholine: ceramide cholinephosphotransferase 4 (SLS4 protein). Site-specific delivery of the circuit into the parasite-infected macrophages could serve as a possible therapeutic intervention of the infectious disease 'Leishmaniasis'.
2024-09-02 | BIOMD0000000584 | BioModels
Project description:Directed evolution in mammalian cells can facilitate the engineering of mammalian-compatible biomolecules and can enable synthetic evolvability for mammalian cells. We engineered an orthogonal alphaviral RNA replication system to evolve synthetic RNA-based devices, enabling RNA replicase-assisted continuous evolution (REPLACE) in live mammalian cells. we employed REPLACE to drive the continuous intracellular evolution of the cancer-related protein MEK1 with the aim of conferring resistance to Cobimetinib. To investigate the accumulation of mutations during this evolutionary process, we conducted amplicon sequencing on experimental materials collected at different stages. The results revealed intricate relationships among different mutations, highlighting the complex nature of the evolutionary landscape.
2024-07-07 | GSE235328 | GEO
Project description:Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test, learn cycle for synthetic biology. They provide a better understanding of synthetic designs and they help identify ways to improve and select designs. While this data is beneficial to design, its collection and analysis is a complex, multi-step process that has implications both on discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, and normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models.
2022-06-16 | GSE206047 | GEO
Project description:In order to test the transcriptome-wide functionality of the identified sRSE instances, we performed an in vivo titration experiment in which synthetic RNA oligonucleotides harboring tandem sRSE1 repeats were used as intracellular decoys that would bind the putative trans factor, preventing it from targeting endogenous transcripts.
2014-07-24 | GSE49609 | GEO
Project description:By combining directed evolution and synthetic biology, we engineered novel synthetic yeasts probiotics for the dynamic modulation of intestinal inflammation. In this experiment we treated TNBS-colitis induced mice with these probiotics and test the amelioration of immune response in the transcriptional level in the colon.
2021-04-22 | GSE152869 | GEO
Project description:An important goal in disease genetics and evolutionary biology is to understand how mutations combine together to alter phenotypes and fitness. Non-additive genetic (epistatic) interactions between mutations occur extensively within and between genes, which makes accurate genetic prediction a difficult challenge. Moreover, for unclear reasons, the interactions between mutations change quite extensively across conditions, cell types, and species, with important consequences for both evolution and precision medicine such as the exploitation of synthetic lethality in cancer. To better understand the plasticity of genetic interactions, we reduced the problem to a minimal system where we combined mutations within a single protein performing a single cellular function. The only perturbation to the system was a change in the expression level of the mutated gene itself. Even in this minimal system, the interactions between mutations were highly plastic, with interactions changing in both magnitude and sign when the expression level was altered. Mathematical modelling revealed the cause of this as the non-linear relationship between the concentration of the protein and the cellular phenotype.These non-linearities are widespread in biology and transform expression level-independent effects of mutations on protein folding and stability into mutation outcomes and interactions that shift and switch as gene expression changes. This plasticity of mutation effects and genetic interactions has important implications for human disease and evolutionary theory.
2019-07-31 | GSE122806 | GEO