Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA116481 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor -activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r=0.98) and the effects of gene knockouts (r=0.8). We stimulate macrophages with 59 different ligands, with and without addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r=0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.
2022-05-09 | GSE202515 | GEO
Project description:Chronic Obstructive Pulmonary Disease (COPD) is a respiratory disorder that is the result of extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD evolves are not fully understood though it is believed that the disease process includes a genetic component since not all smokers develop COPD. To investigate the mechanism leading to the development of COPD/emphysema, we performed an experiment in which whole genome gene expression and several COPD-relevant biological endpoints (MMP-9, MMP activity, TIMP-1 and lung weight) were measured in lung tissue after exposure to two doses of CS for various periods of time. A novel and powerful method, known as reverse engineering and forward simulation (REFS(TM)), was employed to identify key molecular drivers by integrating gene expression data and 4 measured COPD-relevant endpoints. An ensemble of molecular networks was generated using REFS(TM). Simulations showed that this ensemble could successfully recover the measured experimental data for gene expression and measured COPD-relevant endpoints. This ensemble of networks was then further employed to simulate thousands of in silico gene knockdown experiments. Based on the in silico gene knockdown, thirty-three molecular key drivers for the above four COPD-relevant endpoints were identified, with the majority of them being enriched in inflammation, emphysema and COPD.
2014-05-14 | E-MTAB-1426 | biostudies-arrayexpress
Project description:Intrinsically disordered regions (IDRs) are essential for membrane receptor regulation but often remain unresolved in structural studies. TRPV4, a member of the TRP vanilloid channel family involved in thermo- and osmosensation, has a large N-terminal IDR of approximately 150 amino acids. With an integrated structural biology approach, we analyze the structural ensemble of the TRPV4 IDR and identify a network of regulatory elements that modulate channel activity in a hierarchical lipid-dependent manner through transient long-range interactions. A highly conserved autoinhibitory patch acts as a master regulator by competing with PIP2 binding to attenuate channel activity. Molecular dynamics simulations show that loss of the interaction between PIP2-binding site and the membrane reduces the force exerted by the IDR on the structured core of TRPV4. This work demonstrates that IDR structural dynamics are coupled to TRPV4 activity and highlights the importance of IDRs for TRP channel function and regulation.
2023-10-24 | PXD041067 | Pride
Project description:Our findings provide insights into altered bile acid and lipid metabolizing processes in the immature human liver and support the use of iHLC as a relevant in vitro system for modeling the developing liver to study lipid metabolism and PNAC.
2021-04-15 | GSE148790 | GEO
Project description:To better understand the molecular basis of the anticancer effects of acyclic retinoid (ACR), a genome-wide screening was applied to identify novel targets of ACR in human hepatocellular carcinoma (HCC) cells JHH7. Gene expression profiles of JHH7 were measured at 0h, 1h and 4 hours after treatment with1 μM All-trans retinoic acid (AtRA) or 10 μM ACR. Hierarchical clustering with Ward’s method of 44,907 genes demonstrated diverse expression changes in HCC cells treated with ACR for 4h. A total of 973 differentially expressed genes in response to ACR by comparing with AtRA for 4h treatments were identified with a fold change more than 2. Then, network analysis was performed on the altered gene expression profiles using Ingenuity Pathways Analysis (IPA) program. The most highly populated networks were associated with the regulation of cell cycle and DNA replication, as ACR is well known to induce apoptosis and suppress cell proliferation in HCC cells. Moreover, networks related with amino acid metabolism, protein synthesis and lipid metabolism, such as the biological network entitled “Lipid Metabolism, Small Molecular Biochemistry, Vitamin and Mineral Metabolism” were also observed. Of interest, this network contains genes that play critical roles in controlling the development of tissues and organs such as the nuclear orphan receptor nuclear receptor subfamily 2, group F, member 2 (NR2F2), suggesting potential drug targets to prevent/treat HCC. Gene expression profiles of JHH7 were measured at 0h, 1h and 4 hours after treatment with 1μM AtRA or 10 μM ACR.
2015-08-08 | E-GEOD-71856 | biostudies-arrayexpress
Project description:Targeted protein degradation (TPD) has emerged as a powerful approach for removing (rather than inhibiting) proteins implicated in diseases. A key step in TPD is the formation of an induced proximity complex where a degrader molecule recruits an E3 ligase to the protein of interest (POI), facilitating the transfer of ubiquitin to the POI and initiating the proteasomal degradation process. Here, we address three critical aspects of the TPD process using atomistic simulations: 1) formation of the ternary complex induced by a degrader molecule, 2) conformational heterogeneity of the ternary complex, and 3) degradation efficiency via the full Cullin Ring Ligase (CRL) macromolecular assembly. We combine experimental biophysical data---in this case hydrogen-deuterium exchange mass spectrometry (HDX-MS, which measures the solvent exposure of protein residues)---with molecular dynamics (MD) simulations aided by enhanced sampling techniques to accurately predict ternary complex structures at atomic resolution. We demonstrate improved efficiency, accuracy, and reliability in the ternary structure predictions of the bromodomain of the cancer target SMARCA2 with the E3 ligase VHL mediated by three different degrader molecules. The simulations accurately reproduce X-ray crystal structures -- including a new structure that we determined in this work (PDB ID: 7S4E) -- with root mean square deviations (RMSD) of 1.1 to 1.6 \r{A}. The simulations also reveal a structural ensemble of low-energy conformations of the ternary complex. Snapshots from these simulations are used as seeds for additional simulations, where we perform 7.1 milliseconds of aggregate simulation time using Folding@home. The detailed free energy surface captures the crystal structure conformation within a low-energy basin and is consistent with solution-phase experimental data (HDX-MS and SAXS). Finally, we graft a structural ensemble of the ternary complexes onto the full CRL and perform enhanced sampling simulations, which suggest that differences in degradation efficiency may be related to the proximity distribution of lysine residues on the POI relative to the E2-loaded ubiquitin. Several of the predicted ubiquitinated lysine residues have been validated through a ubiquitin mapping proteomics experiment. DOI 10.1038/s41467-022-33575-4
2022-10-01 | PXD033763 | Pride
Project description:Altered adipose lipid mobilization predicts long-term weight gain and impaired glucose metabolism
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Project description:PPARα - let-7 microRNA network in hepatic lipid metabolism [RNA-seq]
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Project description:Regulation of gene expression in biological systems is a complex, nonlinear process composed of context specific interactions, from signaling and transcription to genome modification. Modeling gene regulatory networks (GRNs) can be limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are consequently forced to model covariance between regulatory genes and their targets as a proxy for causal interactions. This in turn complicates validation and reuse of predictive modeling frameworks. To disentangle covariance and casual influence require aggregation of independent and complementary sets of evidences, such as transcription factor (TF) binding and target gene expression. Common approaches include the overlap of evidence to infer causal relations. However the complete state of the system, e.g. TF activity (TFA) is unknown. Other methods tries to estimate these latent features. These models often use linear frameworks that are unable to account for non-linearities, TF-TF interactions, and other higher order features. Deep learning frameworks can be used to model complex interactions between features and capture latent features of higher order. However deep learning methods often discard central concepts in biological systems modeling such as sparsity and latent feature interpretability in favour of increased complexity of the model. In this work we demonstrate that gene regulatory network inference using latent features such as transcription factor activity can be built into a single framework. We present a novel deep learning approach (the Supirfactor framework) that incorporates multiple data-type orthogonal evidence of regulation and maintains interpretable parameter estimates.
2023-01-31 | GSE218089 | GEO