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7,327 results for “pathways”
A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues
<p>Although cellular senescence drives multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), <em>in vivo</em> senescent cell identification remains challenging. Here, we generated a gene set (SenMayo) and validated its enrichment in bone biopsies from two aged human cohorts. We further demonstrated reductions in SenMayo in bone following genetic clearance of senescent cells in mice and in adipose tissue from humans following pharmacological senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from human and murine bone marrow/bone scRNA-seq data. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Using this senescence panel, we were able to characterize senescent cells at the single-cell level and identify key intercellular signaling pathways. SenMayo also represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.</p>
Spatial metabolomics reveal upregulation of several pyrophosphate-producing pathways in cortical bone of Hyp mice
<p>Imaging mass spectrometry data (.imzML format) from bones of <em>Hyp </em>(hypophosphatemia) mice and wild-type controls. Detailed information is given in the publication by Buck & Prade et al. "Spatial metabolomics reveal upregulation of several pyrophosphate-producing pathways in cortical bone of <em>Hyp</em> mice" in JCI Insight.</p>
Supplementary material 1 from: Wilson CE, Castro KL, Thurston GB, Sissons A (2016) Pathway risk analysis of weed seeds in imported grain: A Canadian perspective. In: Daehler CC, van Kleunen M, Pyšek P, Richardson DM (Eds) Proceedings of 13th International EMAPi conference, Waikoloa, Hawaii. NeoBiota 30: 49–74. https://doi.org/10.3897/neobiota.30.7502
Weed seed contaminant species reported in imported grain in a Canadian sampling program 2007–2015 : Explanation note: Complete list of weed seed contaminant species reported in 947 samples of 10 imported grain crops in a Canadian sampling program 2007–2015, cross-listed to number of times reported and crops reported in.
Supplementary material 2 from: Wilson CE, Castro KL, Thurston GB, Sissons A (2016) Pathway risk analysis of weed seeds in imported grain: A Canadian perspective. In: Daehler CC, van Kleunen M, Pyšek P, Richardson DM (Eds) Proceedings of 13th International EMAPi conference, Waikoloa, Hawaii. NeoBiota 30: 49–74. https://doi.org/10.3897/neobiota.30.7502
Frequency distributions showing percentage samples with number of contaminant species reported per sample for 10 imported grain crops examined in a Canadian sampling program 2007–2015 : Explanation note: Ten frequency distribution graphs (one per crop) shown in a multi-panel.
Aviation Fuel Production Pathways from Lignocellulosic Biomass via Alcohol Intermediates – A Technical Analysis - Supplementary Material
<p>Data for the associated publication </p>
Second Opinion Machine Learning for Fast-Track Pathway Assignment in Hip and Knee Replacement Surgery
<p>This is the anonymized training dataset for the article "Second Opinion Machine Learning for Fast-Track Pathway Assignment in Hip and Knee Replacement Surgery: The Use of Patient-Reported Outcome Measures", aimed at developing Machine Learning models for deciding on optimal pathway assignment for total joint replacement patients.</p>
MD simulations associated to the paper "A PDZ tandem repeat folds and unfolds via different pathways"
<p>These are the MD simulation data generated for this study. The files are:</p> <p>x11_plain_simulation: training data, charmm22*+tip3p simulation of X11 PD1-PDZ2</p> <p>x11_random_coil: reference random coil data for X11 PDZ1-PDZ2</p> <p>x11_e0.21_0.335_folding_t310: 200 folding simulations</p> <p>x11_e0.21_0.335_melting_t380: 200 unfolding simulations</p> <p>x11d2_e0.21_0.33_folding_t310: 200 folding simulations for the isolated PDZ2</p> <p>x11_e0.21_0.33_folding_t310_d2open: 200 folding simulations for X11 PDZ1-PDZ2 starting with PDZ1 folded and the C-ter tail bound</p>
ARID1A suppresses R-loop mediated STING-Type I Interferon pathway activation of anti-tumor immunity
<p>Clinical trials have identified ARID1A mutations as enriched among patients who respond favorably to Immune Checkpoint Blockade (ICB) in several solid tumor types independent of microsatellite instability. We show that ARID1A loss in murine models is sufficient to induce anti-tumor immune phenotypes observed in ARID1A mutant human cancers, including increased CD8+ T cell infiltration and cytolytic activity. ARID1A deficient cancers upregulated an interferon (IFN) gene expression signature, the ARID1A-IFN signature, associated with increased R-loops and cytosolic single stranded DNA (ssDNA). Overexpression of the R-loop resolving enzyme, RNASEH2B, or cytosolic DNase, TREX1, in ARID1A deficient cells prevented cytosolic ssDNA accumulation and ARID1A-IFN gene upregulation. Further, the ARID1A-IFN signature and anti-tumor immunity were driven by STING dependent Type I IFN signaling, which was required for improved responsiveness of ARID1A mutant tumors to ICB treatment. These findings define a molecular mechanism underlying anti-tumor immunity in ARID1A mutant cancers.</p>
Dynamics of terminal fraying-peeling and hydrogen bonds dictate the sequential vs co-operative melting pathways of nanoscale DNA and PNA triplexes
<div>##################################################################################################</div> <div> </div> <div>The simulation dataset used for the analysis reported in the manuscript titled -</div> <div> </div> <div> "Dynamics of terminal fraying-peeling and hydrogen bonds dictate the sequential vs co-operative melting pathways of nanoscale DNA and PNA triplexes."</div> <div> by Sandip Mandal, Krishna N. Ganesh, and Prabal K. Maiti*</div> <div> </div> <div> </div> <div>*E-mail: maiti@iisc.ac.in</div> <div>Center for Condensed Matter Theory, Department of Physics,</div> <div>Indian Institute of Science, Bangalore 560012, India</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div>Packages required:</div> <div> Visual Molecular Dynamics (VMD)</div> <div> xmgrace</div> <div> numpy</div> <div> matplotlib</div> <div> scipy</div> <div> tleap/xleap</div> <div> AMBER</div> <div> CPPTRAJ</div> <div> MMGBSA</div> <div> Gaussian09 for partial charge calculation of the PNA protonated cytosine residues</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>Contents:</div> <div> </div> <div>The First main directory contains three folders and a README file--</div> <div>(1)PNA_DNA_PNA_Triplex</div> <div> (2)DNA_DNA_DNA_Triplex</div> <div>(3)Sequence_Dependence</div> <div>(4)README file</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>1. PNA_DNA_PNA_Triplex directory contains sub-directory for Protonated PNA-DNA-PNA triplexes simulation data, such as ---</div> <div>(a) Setup_files</div> <div>(b) Results</div> <div> </div> <div> </div> <div>(a)Setup_files:</div> <div>It contains input coordinates (PDP_18bp_PP.pdb, Protonated_PNA_DNA_PNA.inpcrd ..), topology files, and files required to build to complete </div> <div>system for simulation including library files for non-standard PNA residues and tleap script for the system setup.</div> <div> </div> <div>(b)Results: --folder contains simulation data and analysis scripts for Protonated PNA-DNA-PNA triplexes -----</div> <div> </div> <div>(a)Simulation Trajectories ( It contains simualtion trajectories for three independent trial runs named run1, run2, and run3)</div> <div>(b)RMSD</div> <div>(c)Hbond (hydrogen bonding)</div> <div>(d)Stiffness </div> <div>(e)Free Energy Landscape (FEL)</div> <div>(f)PCA (Principal Component Analysis)</div> <div>(g)Simulation Movies for the Protonated PNA-DNA-PNA triplex</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>2. DNA_DNA_DNA_Triplex directory contains sub-directory for Protonated DNA-DNA-DNA triplexes simulation data, such as ---</div> <div> (a) Setup_files</div> <div> (b) Results</div> <div> </div> <div> </div> <div>(a)Setup_files :</div> <div>It contains input coordinates (DDD_18bp_Protonated_NAB.pdb, 18bp_Protonated_NAB.inpcrd ...) and files required to build to complete</div> <div>system for simulation, including amber input files in the "Sander-Input-files" subdirectory, a script to run the simulation in a GPU clusters, and a leap script for the system setup.</div> <div> </div> <div>(b)Results --folder contains simulation data and analysis scripts for-----</div> <div> </div> <div> (a)Simulation Trajectories ( It contains simulation trajectory for three independent trial runs named run1, run2, and run3)</div> <div> (b)RMSD</div> <div> (c)Hydrogen bonding</div> <div> (d)Stiffness</div> <div> (e)Free Energy Landscape</div> <div> (f)PCA</div> <div> (g)Simulation Movies for the Protonated DNA-DNA-DNA triplex</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div>3. Sequence_Dependence directory contains sub-directory for triplexes with all TAT base triples simualtion data, such as ---</div> <div> (A) DNA_DNA_DNA_with_all_TAT_sequence</div> <div> (B) PNA_DNA_PNA_with_all_TAT_sequence</div> <div> </div> <div>(A)Sub-directory contains simulation data for DNA-DNA-DNA triplexes with 18 TAT base triples (no protonated cytosine residues) ---</div> <div>(a)Set_up_files</div> <div>(b)Trajectory</div> <div>(c)RMSD</div> <div>(d)Hbond</div> <div> </div> <div> </div> <div>(B)Sub-directory contains simulation data for PNA-DNA-PNA triplexes with 18 TAT base triples (no protonated cytosine residues) ---</div> <div> (a)Set_up_files</div> <div> (b)Trajectory</div> <div> (c)RMSD</div> <div> (d)Hbond</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>The trajectories are sampled at 1 ns intervals (since the full trajectory dumped at 10 ps interval, will take huge dataspace) in each trajectory file:</div> <div>(a)The first frame corresponds to the structure immediately after 5 ns NPT equilibriation.</div> <div>(b)The next 200 frames are from the production run, at 1 to 200 ns.</div> <div> </div> <div> </div> <div>References:</div> <div>The DNA-DNA-DNA and PNA-DNA-PNA models were constructed using NAB code [1].</div> <div>System preparation with water and ions are done with the help of tleap/xleap [2].</div> <div>All simulation runs are conducted in AMBER2020 [3].</div> <div>For visualization we have used VMD [4].</div> <div> </div> <div>[1] T. J. Macke and D. A. Case, Modeling unusual nucleic acid structures, 1998</div> <div>[2] D. R. Roe and T. E. Cheatham III, Journal of chemical theory and computation, 2013, 9, 3084–3095</div> <div>[3]D. A. Case, H. M. Aktulga, K. Belfon, I. Ben-Shalom, S. R.Brozell, D. S. Cerutti, T. E. Cheatham III, V. W. D. Cruzeiro,T. A. Darden, R. E. Duke et al., Amber 2021, University of California, San Francisco, 2021</div> <div>[4] Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 14(1), 33-38 (1996).</div>
Effect of gedunin on cell proliferation and apoptosis impact in skin melanoma cells A431 via PI3K/JNK signaling pathway
Open the record for dataset details and reuse information.
Excessive Firing of Dyskinesia-Associated Striatal Direct Pathway Neurons is Gated By Dopamine and Excitatory Synaptic Input
<p>This folder contains data associated with a submitted manuscript: Ryan, Girasole et al, which was first posted on BioRxiv in 2022. https://www.biorxiv.org/content/10.1101/2022.10.31.514594v1.</p> <p><strong>Files associated with each figure: </strong></p> <p><strong>Figure 1 & Supp Figure 1: </strong></p> <ul> <li>Summary.mat</li> <li>Behavior.mat</li> <li>Data.mat</li> <li>TRAP_InVivo_Analysis.m</li> <li>TRAP_Remove_INs.m</li> </ul> <p><strong>Figure 2 & Supp Figure 2: </strong></p> <ul> <li>See BioStudies for rabies dataset: <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST1374?key=9a1b2103-25a6-49ed-b257-0269d3ea19a6" target="_blank" rel="noopener">https://www.ebi.ac.uk/biostudies/studies/S-BSST1374?key=9a1b2103-25a6-49ed-b257-0269d3ea19a6</a></li> </ul> <p><strong>Figure 3 & Supp Figure 3 & Supp Figure 4: </strong></p> <ul> <li> <p>Fig3_FigS3_FigS4_Data.xlsx</p> </li> </ul> <p><strong>Figure 4 & Supp Figure 5: </strong></p> <ul> <li>Fig4Data.xlsx</li> <li>RNAscopeAnalysis_D1_D2.m</li> <li>RNAdata.mat</li> <li>RNAscopeAnalysis_D1_pDyn.m</li> <li>RNAScopeAnalysis.m</li> <li>pDynRNAdata.mat</li> <li>pDynSummary.mat</li> </ul> <p><strong>Contributions</strong>: </p> <p>Behavioral data was collected and analyzed by Ally Girasole, Michael Ryan, and Emily Twedell.</p> <p>In vivo electrophysiological data was collected and analyzed by Michael Ryan and Ally Girasole.</p> <p>Ex vivo electrophysiological data was collected and analyzed by Ally Girasole, Michael Ryan, Emily Twedell, and Alexandra Nelson.</p> <p>Anatomical tracing and histology data was collected and analyzed by Ron Paletzki, Chip Gerfen, Michael Ryan, Ally Girasole and Rea Brakaj.</p> <p>RNA Scope data was collected and analyzed by Andrew Flores, Mike Ryan, and Tom Hnasko.</p>
PPR score of all STRING genes to canonical pathways
<p>PPR score of all genes in the STRING network (edge score>0.7) to canonical pathways.</p>
Kroll et al., 2024. Behavioural pharmacology predicts disrupted signalling pathways and candidate therapeutics from zebrafish mutants of Alzheimer's disease risk genes
<p>Data repository for</p> <p>François Kroll, Joshua Donnelly, Joshua Donnelly, Güliz Gürel Özcan, Eirinn Mackay, Jason Rihel</p> <div> <div><strong>Behavioural pharmacology predicts disrupted signalling pathways and candidate therapeutics from zebrafish mutants of Alzheimer’s disease risk genes</strong></div> <br> <div>eLife, 2024.</div> <br> <div><a href="https://doi.org/10.7554/eLife.96839.1">https://doi.org/10.7554/eLife.96839.1</a></div> <div> </div> <div>Code is found in the <a href="https://github.com/francoiskroll/ZFAD">GitHub repository</a>. Please see notes there.</div> </div> <p>___</p> <p>Contact:</p> <p>Twitter: @francois_kroll</p> <p>Email: francois@kroll.be</p>
Unveiling a Common Phase Transition Pathway of High-Density Amorphous Ices through Time-Resolved X-ray Scattering
<p>XFEL Pump-Probe experiments on VHDA.</p> <p><strong><span>DOI: 10.1063/5.0216904</span></strong></p> <p>The data that support the findings of this study are available here.</p> <p> </p>
A Riskscore Model for Predicting Survival, Tumor Microenvironment, Immunotherapy and drug sensitivity of Lung Squamous Cell Carcinoma Based on PI3K/AKT/MTOR Pathway-Related Genes
<p>Firstly, the data we provide is the raw data downloaded from the TCGA database. Secondly, we provide the following explanations for the raw data, taking Figure 1 as an example:</p> <p>First, we selected a LUSC dataset from the TCGA database that includes RNA-seq data (FPKM values) and clear clinical information, consisting of 51 normal samples and 501 LUSC samples. The raw data <span>can be found in</span> the "mRNA" document in the "Fig.1" file<span>, and t</span>he data includes TCGA <span>id</span> information.</p> <p>Next, the data was imported into "mRNA_edgeR" to analyze whether the RNA-seq data from these 552 samples meet the criteria of |logFC| > 0.585 and FDR < 0.05, <span>and the results are shown in</span> the "diffSig" table. Subsequently, the genes in the "diffSig" table were intersected with 105 PAGs, <span>and</span> 44 key PAGs <span>were identified, </span>as shown in Figure 1.</p> <p>The <span>other</span> figures can <span>also </span>be reproduced sequentially based on the methods described <span>above</span>.</p> <p>Furthermore, due to the limited number of LUSC patients, the clinical information in the database is relatively incomplete. Hence, the clinical information that we provide constitutes the entire content available in the database.</p>
Exploring the Molecular Pathways of the Activation Process on PPARγ Recurrent Bladder Cancer Mutants
<p>The intricate involvement of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in vital processes such as glucose homeostasis and adipogenesis is well established. However, its multifaceted role in cancer, particularly in luminal bladder cancer, remains a subject of intense debate. In this context, PPARγ’s overexpression and activation have been implicated in tumorigenesis. Notably, specific gain-of-function mutations (M280I, I290M, and T475M) within PPARγ’s ligand-binding domain have been pinpointed in bladder cancer, correlating with receptor activation. Nonetheless, the underlying molecular pathways prompted by these mutations remain obscure. Here, we employed a dual-basin structure-based model (db SBM) to unveil the intricate conformational dynamics that underlie the transition between PPARγ’s inactive and active states. Additionally, we explored the effects of the M280I, I290M, and T475M mutations on this pivotal process. Our findings concur with existing literature, revealing heightened ligand-independent transcriptional activity in the I290M and T475M mutants compared to other variants. Importantly, both mutants displayed enhanced stabilization of the active state relative to the wild-type receptor, and the I290M mutation promoted a remarkably specific transition route, rendering it a prime candidate for further investigation. Electrostatic analysis pinpointed the pivotal role of residues K303 and E488 in the activation cascade of I290M. This insight was substantiated by biophysical assays, as disruption of the K303-E488 interaction curtailed the thermal stabilization characteristic of the I290M mutation. These findings designate K303 and E488 as potential targets for inhibitors aimed at modulating PPARγ activation, with promising implications for refining bladder cancer prognosis. In sum, our study highlights the remarkable predictive capabilities achieved through the integration of simulation and cheminformatics methods, validated by biochemical experiments, to gain deeper insights into molecular mechanisms of activation and identify target residues for protein modulation.</p>
Transcriptome profiling associated with CARD11 overexpres-sion in Colorectal Cancer implicates a potential role for Tumour Immune Microenvironment and Cancer pathways modulation via NF-κB
<p>tables for CARD11</p>
Lawsonia intracellularis regulates Nuclear Factor-KB signalling pathway during infection
<p><strong><em><span>raw data to support the figures and information shown in Lawsonia intracellularis</span></em><span> regulates Nuclear Factor-</span><span>k</span><span>B signalling pathway during infection</span></strong></p>
JM-20 administration to animals with lesion of the nigrostriatal dopamine pathway induced by 6-hydroxydopamine, partially reverses motor damage and oxidative stress
<p><strong><span>Abstract</span></strong></p> <p><span>Previous studies have shown that JM-20, a new chemical hybrid molecule, protects against rotenone and 6-hydroxydopamine <span>(6-OHDA)</span> neurotoxicity. Also, we demonstrated that JM-20 blocks the formation of toxic alpha-synuclein aggregated species and <em><span>aminochrome </span></em>cytotoxicity. The present study sought to determine the neuroprotective property of JM-20 in animals with a partial <span>lesion of the nigrostriatal dopamine pathway induced by 6-OHDA </span>. For <em><span>in vivo</span></em> studies, adult male Wistar rats were lesioned in the right <em><span>substantia nigra pars compacta</span></em> (SNpc) upon the administration of 6-OHDA. Fifteen days after surgery, the animal’s asymmetry levels were assessed. Those with asymmetry values higher than 50% were divided into two groups: animals that did not receive any treatment and those that were administered with JM-20 (40 mg/kg, intragastric via gavage) for 27 days. Every seven days, the asymmetry values of the animals were analyzed until day 42 after the surgery. At the end of the experiment, the animals were euthanized and the SNpc and striatum were taken out for the analysis of oxidative stress. Our results reveal a behavioral function progressively recovered in the JM-20-treated animals, diminishing the percentage of motor asymmetry. Also, it ameliorates the oxidative stress in the SNpc and the striatal tissue of these animals. Our study provides the preclinical evidence to support the long term neuroprotective potential of JM-20 in 6-OHDA hemiparkinson's rat model, pointing out to its possible use as a disease-modifying agent in PD.</span></p>
Raw data and metadata associated with the manuscript: "Organ and ontogeny-specific steroidal glycoside diversity is associated with differential expression of steroidal glycoside pathway genes in two Solanum dulcamara leaf chemotypes"
<p>Raw LC-MS and RT-qPCR data and metadata associated with the manuscript: "Ontogeny and organ-specific steroidal glycoside diversity is associated with differential expression of steroidal glycoside pathway genes in two <em>Solanum dulcamara</em> leaf chemotypes", accepted at Plant Biology.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.