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443 results for “Metabolic Reprogramming”
NANOG metabolically reprograms tumor-initiating stem-like cells in oxidative phosphorylation and fatty acid metabolism
GEO Series GSE68237. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Cachexia-induced reprogramming of visceral organ metabolism by human pancreatic cancer xenografts
<p>We have uploaded the raw MRS data used in this study. The data were obtained through dual-phase extraction from the heart, lungs, kidneys, pancreas, liver, and spleen samples. Each sample was reconstituted in 650 μL of phosphate-buffered D₂O (90:10 D₂O:H₂O, pH 7.4) with trimethylsilylpropanoic acid (TSP), and analyzed by ¹H MRS.All spectra were acquired using a Bruker 750 MHz NMR spectrometer using single pulse sequence under consistent experimental conditions: a spectral width of 15,495.86 Hz, 64K data points, a 90° flip angle, a relaxation delay of 10 seconds, an acquisition time of 2.11 seconds, and 64 scans with 8 dummy scans. The MRS data were analyzed and quantified using TOPSPIN 4.0.6 software. Spectral processing included zero-filling to 64K and applying an exponential line broadening of 0.3 Hz before Fourier transformation. Peak integration was performed using AMIX software, and the area under each peak was used for quantitative analysis after normalization to the internal standard, TSP (trimethylsilylpropanoic acid). <strong> </strong>We identified, for the first time, the profound effects of pancreatic cancer induced cachexia on visceral organ weight and metabolism that can lead to severe consequences in organ function ranging from affecting pathways involved in tissue regeneration and resolving inflammation, to altering IL-4 production in cachectic mice. These results highlight the damaging systemic changes in visceral organ metabolism that occur with cancer induced cachexia that may lead to metabolic interventions to reduce morbidity and mortality. </p> <p> </p> <p> </p>
Metabolomics Data - Integrated multi-omics analysis using MENTOR reveals metabolic reprogramming in the Niemann-Pick type C mouse brain
<p>Mice were euthanized with isoflurane followed by decapitation. Brains were collected rapidly from 7 week old mice from each genotype (WT and Npc1-/-), and the forebrains were quickly dissected and flash frozen in liquid nitrogen. Tissue samples were removed from -80 °C storage and maintained on wet ice throughout the processing steps. Tissues were carefully weighed to 30 mg +/- 2 mg and the extraction solvent (1:1:1:1: Methanol:Acetone:Acetonitrile:Water) containing internal standards was scaled to the tissue weight (30:1). Tissue was disrupted using a probe sonicator at 40% output power, 40% duty cycle for 20 seconds. Samples were allowed to rest on wet ice for 10 min, then centrifuged at 4° C, 14,000 rpm for 10 min. 10 µL of each sample was removed and pooled in a separate autosampler vial for quality control purposes. 200 µL of supernatant was transferred to an autosampler vial and brought to complete dryness using a nitrogen drier in ambient conditions. Samples and pools were reconstituted with 100 µL and 150 µL of water: methanol (8:2 by volume).</p> <p>Analysis was performed on an Infinity Lab II UPLC coupled with a 6545 QTof mass spectrometer (Agilent Technologies) using a JetStream ESI source in negative mode. The following source parameters were used: Gas Temp: 250 °C, Gas Flow: 13 L/min, Nebulizer: 35 psi, Sheath Gas Temp: 325 °C, Sheath Gas Flow: 12 L/min, Capillary: 3500 V, Nozzle Voltage: 1500 V.</p> <p>The UPLC was equipped with a 10-port valve configured to allow the column to be either eluted to the mass spectrometer or back-flushed to waste. The chromatographic separation was performed on an Agilent ZORBAX RRHD Extend 80Å C18, 2.1 × 150 mm, 1.8 μm column with an Agilent ZORBAX SB-C8, 2.1 mm × 30 mm, 3.5 μm guard column. The column temperature was 35 °C. Mobile phase A consisted of 97:3 water/ methanol and mobile phase B was 100% methanol; both A and B contained tributylamine and glacial acetic acid at concentrations of 10mM and 15mM, respectively. The column was back-flushed with mobile phase C (100% acetonitrile, no additives) between injections for column cleaning.<br>The LC gradient was as follows: 0-2min, 0%B; 2-12 min, linear ramp to 99%B; 12-17.5 min, 99%B. At 17.5 min, the 10-port valve was switched to reverse flow (back-flush) through the column, and the solvent composition changed to 99%C. From 20.5-21 min the flow rate was ramped to 0.8 mL/min, held until 22.5 min, then reduced to 0.6mL/min. From 22.7-23.5 min the solvent was ramped from 99% to 0% C while flow was simultaneously ramped down from 0.6-0.4mL/min and held until 29.4 min, at which point flow rate was returned to starting conditions at 0.25mL/min. The 10-port valve was returned to restore forward flow through the column at 28.5 min. An isocratic pump was used to introduce reference mass solution through the reference nebulizer for dynamic mass correction. Total run time was 30 min. The injection volume was 5 uL.</p> <p>Data analysis for this platform follows a hybrid targeted/non-targeted approach. Semi-quantitative data for known compounds is obtained by manual integration using Profinder v8.00 (Agilent Technologies, Santa Clara, CA.) Metabolites were identified by matching the retention time (+/- 0.1 min), mass (+/- 10 ppm) and isotope profile (peak height and spacing) to authentic standards. Non-targeted data analysis was performed using Agilent’s MassHunter Find by Molecular Feature workflow (v7.0) with recursion using Agilent’s Mass Profiler Pro (v8.0).</p> <p>A combined feature set was generated by merging untargeted features and named metabolites into a single feature list. The combined feature set underwent data reduction using Binner <em>(M. Kachman et al., 2020)</em>. Briefly, Binner first performs RT-based binning, followed by clustering of features by Pearson’s correlation coefficient, and the assignment of isotopes, adducts or in-source fragments by searching for known mass differences between highly correlated features. After Binner data reduction in-house software was used to search Refmet (https://www.metabolomicsworkbench.org/databases/refmet/index.php) to provide MS1 <em>(L. W. Sumner et al., 2007)</em> Level III identifications, or to an in-house library of authentic standards to provide MS1 Level I identifications.</p> <p>Iterative Data Dependent Acquisition (iDDA) ms/ms analysis was performed on the pooled sample material. iDDA captures ms/ms in stepwise fashion, with rolling excluded precursors. For untargeted platforms, we collect 8 rounds of iDDA at 3 different collision energies. At each collision energy, ~8000 ms/ms spectra are collected, which represent ms/ms spectra for approximately 75-95% of the features obtained by the untargeted data analysis. Analysis of iDDA spectra using NIST2020 was performed to provide MS1 Level II identifications for statistically significant features.</p>
Periodic dietary restriction of animal products induces metabolic reprogramming in humans with effects on health
<p>We conducted metabolomic and proteomic profiling in 411 healthy individuals, of whom almost a half follow a structured dietary pattern of animal product restriction.<br>Metabolite levels were measured on the Nightingale panel. Protein levels were measured on the Olink 1536 panel. Following QC, we conducted differential abundance analysis using limma (statistical software package in R) for metabolites and proteins, as described in our manuscript. Here we have deposited limma output results, annotated for each comparison made (eg. PRT2vsPRT1 for PR individuals at TImepoint 2 vs. Timepoint 1). For more details, please refer to the manuscript</p> <div> </div> <div>For metabolomics the files include:</div> <div>1. Nightingale_LIMMA_777ind_PRT2vsPRT1.txt 2. Nightingale_LIMMA_777ind_NRT2vsNRT1.txt 3. Nightingale_LIMMA_777ind_PRT1vsNRT1.txt 4. Nightingale_LIMMA_777ind_PRT2vsNRT2.txt</div> <div> </div> <div>For proteomics the files include:</div> <div>1. Olink_LIMMA_793ind_NRT2vsNRT1.txt 2. Olink_LIMMA_793ind_PRT1vsNRT1.txt 3. Olink_LIMMA_793ind_PRT2vsNRT2.txt 4. Olink_LIMMA_793ind_PRT2vsPRT1.txt</div> <div> </div> <div>The columns in the files refer to:</div> <div><strong>logFC:</strong> Log-fold change, indicating the magnitude of change in gene expression between conditions. <strong>CI.L and CI.R:</strong> Confidence intervals for the log-fold change. <strong>AveExpr:</strong> Average expression level of the gene across conditions. <strong>t:</strong> t-statistic, measuring the significance of the difference in expression between conditions. <strong>P.Value: </strong>P-value, indicating the probability of observing the data if the null hypothesis (no difference in expression) is true. <strong>adj.P.Val:</strong> Adjusted P-value, considering multiple testing corrections. <strong>B:</strong> B-statistic, a measure of the strength of evidence for differential expression. <strong>metabolite:</strong> metabolite that was measured.<strong>OlinkID: </strong>protein ID provided by Olink <strong>UniProt: </strong>Uniprot ID <strong>Assay: </strong>gene name</div> <p> </p>
Th Tl Xb Prescription on Reprogramming of Lipid Metabolism and Endothelial Injury for Cerebral Infarction Patients
ClinicalTrials.gov study NCT06549582. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Intestinal Remodeling And Reprogramming of Glucose Metabolism Following Laparoscopic Roux-en-Y Gastric Bypass
ClinicalTrials.gov study NCT02288351. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Targeting NPM1 Epigenetically Promotes Post-infarction Cardiac Repair by Reprogramming Reparative Macrophage Metabolism [CUT&Tag]
GEO Series GSE230938. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Lipid metabolism reprograming by SREBP1-PCSK9 targeting sensitizes pancreatic cancer to immunochemotherapy
GEO Series GSE290259. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Indole-3-lactic acid suppresses colorectal cancer via metabolic reprogramming
GEO Series GSE271802. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
The N-terminal domain of gasdermin D induces liver fibrosis by reprogrammed lipid metabolism
GEO Series GSE252989. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Mitohormesis reprograms macrophage metabolism to enforce tolerance [BMDM_OHestrogen_RNAseq]
GEO Series GSE169727. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Microenvironmental Stiffness Induces Metabolic Reprogramming in Glioblastoma
GEO Series GSE239610. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
DNA Methylation Reprograms Metabolic Gene Expression in End-Stage Human Heart Failure
GEO Series GSE123976. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
A critical role for Hepatocyte Nuclear Factor 4 alpha in polymicrobial sepsis-associated metabolic reprogramming and death: ATAC-seq after CLP sepsis model
GEO Series GSE244821. Mus musculus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Mitohormesis reprograms macrophage metabolism to enforce tolerance.
GEO Series GSE169731. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Paternally-induced transgenerational environmental reprogramming of metabolic gene expression in mammals (Affymetrix)
GEO Series GSE25896. Mus musculus. 8 samples. Type: Expression profiling by array.
Schistosoma mansoni infection metabolically reprograms the myeloid lineage in a mouse model of metabolic disease
GEO Series GSE155175. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Skatole attenuates osteoarthritis by protecting chondrocytes and mediating macrophage repolarization through regulating the NFκB/MAPK signaling pathway and metabolic reprogramming II
GEO Series GSE283748. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
MET Inhibition Elicits PGC1α Dependent Metabolic Reprogramming in Glioblastoma
GEO Series GSE134676. Homo sapiens. 4 samples. Type: Expression profiling by array.
Co-targeting of HDAC, PI3K, and Bcl-2 Results in Metabolic and Transcriptional Reprograming and Decreased Oxidative Phosphorylation in Acute Myeloid Leukemia
GEO Series GSE206494. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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