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Celastrol

Celastrol is a pentacyclic triterpene derived from the Thunder God Vine (Tripterygium wilfordii) that has been extensively studied for its diverse pharmacological properties.
This natural compound exhibits potent anti-inflammatory, antioxidant, neuroprotective, and antitumor activities, making it a promising therapeutic candidate for a variety of diseases.
Celastrol has been shown to modulate multiple signaling pathways, including the NF-kB, Nrf2, and heat shock response pathways, which contribute to its broad range of biological effects.
Reasearchers are actively investigating the use of Celastrol for the treatment of conditions such as rheumatoid arthritis, neurodegenerative disorders, cancer, and metabolic syndromes.
However, the complex mechanisms of action and variable experimental protocols in Celastrol research have presented challenges in terms of reproducibility and accuracy.
PubCompare.ai leverages cutting-edeg AI technology to help researchers navigate these complexities and identify the most reliable and accurate protocols from the literature, pre-prints, and patents, in order to enhance the reproducibility and accuracy of Celastrol research.

Most cited protocols related to «Celastrol»

1
Rank-Rank Hypergeometric Overlap Analysis
Cited 68 times
Generally, we recommend sample permutation analysis as a way of assessing the overall significance of a hypergeometric map. When permutations are performed by randomly reassigning the sample class labels and recalculating the ranked gene lists, gene–gene correlations within the data are preserved and thus this method provides a reliable estimate of how unlikely an overlap result could be attained by random chance (8 (link)). Note that shuffling gene labels instead of sample labels does not maintain these gene–gene correlations within each sample and thus simply recreates the hypergeometric distribution. Permutation-based correction requires relatively high computation time [O(Npermutations)] and a high enough number of samples in each class of the profiling experiments to allow for sufficient distinct permutations. We use summary statistics of the rank–rank overlap maps (described below) to screen through the permutated-sample maps and determine the frequency of permutation instances that had an optimal overlap more significant than in the true case to estimate the probability of observing the true amount of overlap by chance. For a comparison of gene lists of length 5000 and a rank threshold step size of 50, it takes ∼15 s to calculate the 10 000 [(5000/50)2] hypergeometric CDF results required to create one RRHO map using an Intel Xeon 3.2 GHz processor, thus requiring ∼4 h to create and analyze 1000 permutation maps on a single computational node.
We have designed summary statistics for rank–rank hypergeometric analysis based on our experience using gene-expression microarray data. The most straightforward summary statistic of a rank–rank hypergeometric map is the point with the maximum absolute log P-value, which represents the rank threshold pair that gives the most significant hypergeometric overlap in the experiments being compared. When genes are ranked using a direction-signed metric, there are often two distinct signals in the map: one corresponding to overlap in the tops of the lists (signal in the bottom left corner of the RRHO map, from genes upregulated in both experiments) and one corresponding to overlap in the bottoms of the lists (top right corner, co-downregulated genes) (see example in Figure 1B). This occurs when using lists with overlap at each end, but little rank correlation in the middle of the lists typically due to random ordering of the many genes that are not differentially expressed in the two individual experiments. In these cases, it can be helpful to use measures that summarize the top–top and bottom–bottom signals separately, namely the maximal absolute log P-value in these two regions of the map that correspond to genes changing in the same direction. We use these two maxima both individually and as a sum (see User-Guide Figure 2 in Supplementary Data and the application of this approach in Figures 4 and 5).

RRHO Maps are a sensitive method for detection and visualization of overlap in expression data. Three representations of overlapping published cancer-related gene-expression signatures: signatures with strong (A–C), medium (D–F) and weak (G–I) overlap represented as metric scatter plots (A, D and G), rank–rank scatter plots (B, E and H) and hypergeometric overlap maps (C, F and I). The first row of metric scatter plots show genes plotted by their direction-signed (up, positive; down, negative), log10-transformed t-test P-values in each experiment; genes significantly changing in the same direction in both experiments are in Cartesian quadrants I and III and in opposite directions in quadrants II and IV. The Spearman’s ρ and Kendall’s τ rank correlation coefficients are indicated. The second row of rank–rank scatter plots show each gene plotted by its rank based on this metric. This representation spreads the genes more evenly across the plot and allows for assessment of overlap by increases in local density. The resulting plots show a higher density of genes along the diagonal in both the strong and medium overlap cases, especially at the bottom left and top right regions. The last row shows the rank–rank hypergeometric heatmap for each of these comparisons, where the overlap is represented statistically based on the hypergeometric distribution allowing visualization of any signal, even those that are relatively weak but significant. The log10-transformed hypergeometric P-values are indicated in the color scale bar with negative values indicating under-enrichment. Sample permutation P-values based on the sum of the signal in the bottom left and top right regions are indicated (perm pval).

RRHO identifies statistically significant overlap between expression signatures supporting or generating biological hypotheses. (A) High overlap in gene-expression changes between mouse models of prostate neoplasia driven by PTEN and AKT, both in the PI3K signaling pathway, identifying genes consistently modulated by two different perturbations in the same pathway during tumorigenesis [hypergeometric P-value (HP) 10−27] (22 (link),26 (link)). (B) Increasing overlap of castration signature with PTEN-knockout-driven prostate cancer over time (minimum HP 10−27) (22 (link),27 (link)). Each map shows the overlap between non-cancerous prostate tissue following castration in a mouse (compared to prostate tissue from uncastrated mice) and a mouse model of prostate cancer driven by PTEN loss (compared to prostate tissue from wild-type littermates). The degree of overlap increases with time and is reversed when testosterone is given for 3 days. (C) Significant overlap between a stem cell signature from murine mammary glands and BCR-ABL fusion onco-protein signature (HP 10−8) (30 (link),31 (link)). (D) High overlap between gene-expression changes driven by overexpression of KRAS2 oncogene in a cell line and by mutated KRAS2 in human lung tumors, identifying signaling events downstream of this oncogene that are potentially clinically relevant and can be studied in the cell line model (HP 10−24) (35 (link)). (E) High overlap between treatment with the small-molecule inhibitor imatinib in two leukemia patient populations, identifying higher confidence markers for drug response (HP 10−141) (21 (link),36 (link)). (F) Significant overlap between cell line-derived profiles from different tissue types based on sensitivity to the small-molecule inhibitor dasatinib, identifying possible adaptation mechanisms which can be targeted for higher drug efficacy (HP 10−33) (38 (link),39 (link)). The tick mark on each axis indicates the point in the ranked gene-expression signature list where the direction of differential expression switches. A permutation P-value based on the sum of the signal in bottom left and top right regions (perm pval) or when permutations are not possible the maximum of the BY-corrected RRHO map (max BY) is indicated.

RRHO yields comparable significance results to GSEA while adding a 2D perspective. (A) Schematic showing how RRHO and GSEA have been applied for comparing two continuous expression signatures. A previous application of GSEA used statistical methods (SAM algorithm) to choose an appropriate fixed threshold to define two ‘gene sets’ of up- and downregulated genes from their mouse model of lung cancer. These gene sets were then used in GSEA to search for overlap with the expression profile from human tumors treated as a continuous lists (SAM/GSEA approach) (1 (link)). RRHO treats both signatures as continuous by stepping through all possible threshold pairs in both ranked gene lists to create a hypergeometric overlap map. The two algorithms use different but highly related enrichment statistics, the hypergeometric distribution P-value for RRHO and the Kolmogorov–Smirnov statistic for GSEA (see ‘Materials and Methods’ section). Both approaches employ multiple hypothesis corrections through either permutations or analytical corrections, performing the sliding threshold(s) step in all permutations. (B and C) RRHO maps showing the overlap between a KRAS2-driven mouse model profile (x-axes) and two human lung cancer profiles (y-axes), one for lung tumorigenesis in general and one specific to KRAS2 status within tumor subgroups. Red dashed lines approximate the threshold chosen to represent the mouse model gene set in the published GSEA-based analysis and relate to the mountain plot output produced by GSEA. (D) Comparison of permutation P-values between the SAM/GSEA and RRHO approaches. Permutation P-values from our best recreation of Sweet-Cordero SAM/GSEA analysis mouse model defined up and down (dn) gene sets are listed juxtaposed to permutation P-values from summary statistic-based interpretations of the RRHO maps for the same data. Up results are from analysis of genes upregulated in both experiments, and likewise for down. Up+dn RRHO results simultaneously consider both directions by calculating the sum of the maximal absolute log P-value from both the up–up and down–down regions of the heatmap during the permutation analysis (see ‘Materials and Methods’ section).

Using RRHO to survey a compendium of gene-expression signatures. (A) We used both RRHO and a GSEA-based approach to compare the overlap of a gedunin treatment gene-expression profile with a panel of small molecule drug treatment signatures from the Cmap database. Mean Cmap scores are reported for multiple instances of a single drug (x-axis) using a gedunin signature of 50 up- and 50 downregulated genes. Benjamini-Yekutieli (BY)-corrected RRHO maps for multiple instances were created individually and combined into a composite map using a pixel-by-pixel mean; the maximum positive or minimum negative value (whichever has higher absolute value) is reported (y-axis). The Spearman rank correlation coefficient between the overlap measures of these two scoring techniques shows overall positive correlation (ρ = 0.46). Estimated significance thresholds for each algorithm are indicated with red dashed lines. (B–E) Mean BY-corrected RRHO maps showing gene-expression overlap of gedunin with the HSP90 inhibitors celastrol (B) and 17-AAG (C), the lysosome uptake inhibitor monensin (D), and the PI3 kinase inhibitor wortmannin (E). These drugs show statistically significant overlap by RRHO analysis, but varying degrees of overlap by Cmap analysis. Note that in the two cases where RRHO analysis detects signal while the GSEA-based approach does not (D and E), the maximal overlap is outside the top 50 genes used in the GSEA-based approach (i.e. the signal is further from the lower left or upper right corner than in the other cases).

Plaisier S.B., Taschereau R., Wong J.A, & Graeber T.G. (2010). Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Research, 38(17), e169.
Publication 2010
2
Validating Microarray Results by RT-PCR
Cited 11 times
The significance of array results has been checked and validated by RT-PCR analysis for selected significantly regulated genes throughout the array study. The RT-PCR was performed with the same samples used for microarray according to QuantiTect SYBR Green Kit (Qiagen, Germany).37 (link),41 (link),44 (link),45 (link)
Youns M., Askoura M., Abbas H.A., Attia G.H., Khayyat A.N., Goda R.M., Almalki A.J., Khafagy E.S, & Hegazy W.A. (2021). Celastrol Modulates Multiple Signaling Pathways to Inhibit Proliferation of Pancreatic Cancer via DDIT3 and ATF3 Up-Regulation and RRM2 and MCM4 Down-Regulation. OncoTargets and therapy, 14, 3849-3860.
Publication 2021
Genes Microarray Analysis Reverse Transcriptase Polymerase Chain Reaction SYBR Green I
3
Liver and Hypothalamus Transcriptomic Analysis
Cited 9 times

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Publication 2015
4
Enriched Neuronal Culture Protocols
Cited 7 times
Embryonic cortical neurons were isolated by standard procedures. E16.5 embryonic cerebral cortices were treated with 0.25% Trypsin-EDTA and dissociated into single cells by gentle trituration. Cells were suspended in Neurobasal medium supplemented with B27 and 2 mM GlutaMAX, then plated on coverslips or dishes coated with poly-L-Lysine (0.05 mg/mL) diluted in boric buffer. Enrichment of neuronal culture was performed using both a previously reported method and a modified protocol. For the previously reported method [5 (link)], 5 μM 5-fluoro-2'-deoxyuridine (FdU) from Sigma (F0503, St. Louis, MO, USA) was added into the culture at DIV4. Cultures were then replenished by replacing half of the volume of medium, but still containing 5 μM FdU, every 3–4 days until DIV14. For our modified protocol, 1 or 2 μM FdU added at DIV4 and incubated for 24hr to kill the proliferating cells. Medium containing FdU was then replaced with fresh medium without FdU (old to new NB medium in 1:1 ratio). Half of the medium was then replaced every 5 days until at least 14 days in vitro (DIV) before any treatment.
Murine neuroblastoma N2a cells were cultured in standard DMEM media supplemented with 10% FBS for routine passage. N2a cells and primary cortical neurons were treated with lipopolysaccharide (LPS, 1 μg/ml) (L2880, Sigma), tumor necrosis factor alpha (TNFα, 100 ng/ml) (#1050, BioVision, Milpitas, California, USA) or interleukin 1 beta (IL1β, 50ng/ml) (#4128, BioVision) for 1 and 24 hours. Celastrol (Sigma) was added to the cultures for 24 hours to study NFκB signaling pathways. For histological study, cells were washed with PBS and fixed in 4% PFA for 15 minutes. After rinsing in PBS, cells were immersed in 0.1% PFA for long-term storage.
Hui C.W., Zhang Y, & Herrup K. (2016). Non-Neuronal Cells Are Required to Mediate the Effects of Neuroinflammation: Results from a Neuron-Enriched Culture System. PLoS ONE, 11(1), e0147134.
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Publication 2016
5-FdU 5-fluoro-2'-deoxyuridine boric acid Buffers celastrol Cells Cortex, Cerebral Culture Media Edetic Acid Embryo Hyperostosis, Diffuse Idiopathic Skeletal Interleukin-1 beta Lysine Mus Neuroblastoma Neurons Poly A RELA protein, human Signal Transduction Pathways TNF protein, human Trypsin
5
Evaluating Celastrol and Radiation Effects on Prostate Tumor
Cited 5 times

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Publication 2009
angiogen Animals Apoptosis Biotin Body Weight Buffers celastrol Cells cremophor deoxyuridine triphosphate DNA Nucleotidylexotransferase Eosin Ethanol Hematoxylin Mice, Nude Mus Neoplasms Paraffin Embedding paraform PC 3 Cell Line Proteins Psychological Inhibition Radiation Radiotherapy Sensitivity Training Groups Sulfoxide, Dimethyl Tail Transferase Woman X-Rays, Diagnostic

Most recents protocols related to «Celastrol»

1
Biotin-Celastrol Affinity Purification
Neuron lysates were incubated with biotin, celastrol‐biotin, or a combination of celastrol‐biotin and celastrol at 4 °C overnight. The lysates were subsequently subjected to pull‐down using streptavidin‐conjugated beads (Softlink soft release Avidin Resin, PROMEGA) at 4 °C for an additional 4 h. After extensively washing with PBS, the beads were boiled in 2 × loading buffer, and the supernatants were used for western blot analysis of EPAC‐1.
Li X S.r., Liu W., Jiang G., Lian J., Zhong Y., Zhou J., Li H., Xu X., Liu Y., Cao C., Tao J., Cheng J., Zhang J.H, & Chen G. (2024). Celastrol Ameliorates Neuronal Mitochondrial Dysfunction Induced by Intracerebral Hemorrhage via Targeting cAMP‐Activated Exchange Protein‐1. Advanced Science, 11(19), 2307556.
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Publication 2024
2
Celastrol Targets Prediction for Rheumatoid Arthritis
The database used in this study is public. The structure of Celastrol was searched from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and saved as “sdf” format file. The “sdf” file were then input into Pharmmapper to predict the potential targets of Celastrol 121 (link). In the GeneCards database (https://www.genecards.org/) 122 (link), OMIM database (https://omim.org/) 123 , DisGeNet database (https://www.disgenet.org/) 124 (link), and DrugBank database (http://www.drugbank.ca) 125 (link), “Rheumatoid Arthritis” was used as the key word to search for RA genes, and the results were combined and deduplicated to obtain the RA-related gene set. The Celastrol target set and RA-related gene set were intersected, and the targets in the intersection set were considered as the target of Celastrol in the treatment of RA (that is Celastrol-RA targets).
Zeng L., Yu G., Yang K., He Q., Hao W., Xiang W., Long Z., Chen H., Tang X, & Sun L. (2024). Exploring the mechanism of Celastrol in the treatment of rheumatoid arthritis based on systems pharmacology and multi-omics. Scientific Reports, 14, 1604.
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Publication 2024
3
Celastrol Dosage Effects on ICH
In the experimental study, the mice were divided into three groups receiving celastrol at doses of 1, 2, and 4 mg k−1g. The corresponding concentrations were prepared by dissolving celastrol in DMSO (10 mg mL−1) and then diluting with PBS. Celastrol was administered intraperitoneally for 3 consecutive days after ICH according to different groups. The vehicle group received an intraperitoneal injection of PBS in equal volume.
Li X S.r., Liu W., Jiang G., Lian J., Zhong Y., Zhou J., Li H., Xu X., Liu Y., Cao C., Tao J., Cheng J., Zhang J.H, & Chen G. (2024). Celastrol Ameliorates Neuronal Mitochondrial Dysfunction Induced by Intracerebral Hemorrhage via Targeting cAMP‐Activated Exchange Protein‐1. Advanced Science, 11(19), 2307556.
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Publication 2024
4
Celastrol-RA Protein Interaction Network Analysis
Celastrol-RA targets were input into STRING (https://string-db.org/), the species was limited to “Homo sapiens” or “Mus musculus”, and the minimum interaction threshold was set to “medium confidence” (≥ 0.4) 128 (link). The result file was imported into Cytoscape 3.9.1 software to construct a protein–protein interaction (PPI) network and visualize the results 129 (link). Then Celastrol-RA targets were imported into the Metascape database (https://metascape.org/gp/index.html) 130 (link). “Input as species” and “Analysis as species” were set to Homo sapiens or Mus musculus, and the threshold was set to P < 0.05 for gene ontology (GO) and Kyoto encyelopedia of genes and genomes (KEGG) enrichment analysis., so as to analyze the possible mechanism of Celastrol in the treatment of RA.
Zeng L., Yu G., Yang K., He Q., Hao W., Xiang W., Long Z., Chen H., Tang X, & Sun L. (2024). Exploring the mechanism of Celastrol in the treatment of rheumatoid arthritis based on systems pharmacology and multi-omics. Scientific Reports, 14, 1604.
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Publication 2024
5
Celastrol Docking with EPAC-1 Protein
AutoDock (version 4.2.6) was used to perform molecular docking of celastrol and cAMP‐ EPAC‐1 proteins. To obtain the necessary information about proteins, the PDB format files were accessed from the RCSB Protein Data Bank. The SDF format file of celastrol was retrieved from the NCBI PubChem Compound database (https://www.ncbi.nlm.nih.gov/pccompound/). After solvent and ligand removal, as well as hydrogenation, electron transfer, and other operations, EPAC‐1 protein files were prepared for use as receptors. Then, a PDBQT format file of celastrol was established to serve as the ligand for subsequent molecular docking analysis. The results were analyzed using AutoDockTools (version 1.5.7), while PyMOL (version 2.4.1) was used for visual simulation.
Li X S.r., Liu W., Jiang G., Lian J., Zhong Y., Zhou J., Li H., Xu X., Liu Y., Cao C., Tao J., Cheng J., Zhang J.H, & Chen G. (2024). Celastrol Ameliorates Neuronal Mitochondrial Dysfunction Induced by Intracerebral Hemorrhage via Targeting cAMP‐Activated Exchange Protein‐1. Advanced Science, 11(19), 2307556.
Full text: Click here
Publication 2024

Top products related to «Celastrol»

1
Celastrol by Merck Group
Sourced in United States, Germany
Celastrol is a laboratory reagent produced by Merck Group. It is a triterpenoid compound with a molecular formula of C₂₉H₃₈O₄. Celastrol's core function is as a bioactive molecule for use in biochemical and cell biology research applications.
2
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
3
Trizol reagent by Thermo Fisher Scientific
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
4
Dimethyl sulfoxide (dmso) by Merck Group
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
5
Celastrol by Cayman Chemical
Sourced in United States
Celastrol is a chemical compound that can be used in laboratory settings. It functions as an antioxidant and possesses anti-inflammatory properties. The core function of Celastrol is to serve as a research tool for scientific investigations, without any claims about its intended use.
6
Lipofectamine 2000 by Thermo Fisher Scientific
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Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
7
Prism 8 by GraphPad
Sourced in United States, Austria, Canada, Belgium, United Kingdom, Germany, China, Japan, Poland, Israel, Switzerland, New Zealand, Australia, Spain, Sweden
Prism 8 is a data analysis and graphing software developed by GraphPad. It is designed for researchers to visualize, analyze, and present scientific data.
8
Penicillin streptomycin by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, China, Canada, France, Japan, Australia, Switzerland, Israel, Italy, Belgium, Austria, Spain, Gabon, Ireland, New Zealand, Sweden, Netherlands, Denmark, Brazil, Macao, India, Singapore, Poland, Argentina, Cameroon, Uruguay, Morocco, Panama, Colombia, Holy See (Vatican City State), Hungary, Norway, Portugal, Mexico, Thailand, Palestine, State of, Finland, Moldova, Republic of, Jamaica, Czechia
Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
9
Rpmi 1640 medium by Thermo Fisher Scientific
Sourced in United States, China, Germany, United Kingdom, Japan, France, Canada, Australia, Italy, Switzerland, Belgium, New Zealand, Spain, Israel, Sweden, Denmark, Macao, Brazil, Ireland, India, Austria, Netherlands, Holy See (Vatican City State), Poland, Norway, Cameroon, Hong Kong, Morocco, Singapore, Thailand, Argentina, Taiwan, Province of China, Palestine, State of, Finland, Colombia, United Arab Emirates
RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.
10
Celastrol by InvivoGen
Sourced in United States, France
Celastrol is a triterpenoid compound extracted from the Chinese medicinal plant Tripterygium wilfordii. It functions as a potent inhibitor of the transcription factor NF-κB, which plays a crucial role in inflammatory responses and cell signaling pathways.

More about "Celastrol"

Celastrol, a pentacyclic triterpene derived from the Thunder God Vine (Tripterygium wilfordii), has garnered significant attention in the scientific community due to its diverse pharmacological properties.
This natural compound exhibits potent anti-inflammatory, antioxidant, neuroprotective, and antitumor activities, making it a promising therapeutic candidate for a variety of diseases.
Celastrol has been shown to modulate multiple signaling pathways, including the NF-kB, Nrf2, and heat shock response pathways, which contribute to its broad range of biological effects.
Researchers are actively investigating the use of Celastrol for the treatment of conditions such as rheumatoid arthritis, neurodegenerative disorders, cancer, and metabolic syndromes.
Navigating the complexities of Celastrol research can be challenging, as the mechanisms of action and experimental protocols can vary significantly.
This is where tools like PubCompare.ai come into play.
Powered by cutting-edge AI technology, PubCompare.ai helps researchers identify the most reliable and accurate protocols from the literature, pre-prints, and patents, enhancing the reproducibility and accuracy of Celastrol research.
Researchers utilizing Celastrol in their studies may also employ other common laboratory reagents and techniques, such as FBS (Fetal Bovine Serum) for cell culture, TRIzol reagent for RNA extraction, DMSO (Dimethyl Sulfoxide) as a solvent, Lipofectamine 2000 for transfection, Prism 8 for data analysis, Penicillin/streptomycin for antibiotic supplementation, and RPMI 1640 medium for cell culture.
By leveraging these tools and techniques, researchers can optimize their Celastrol-related experiments and contribute to the growing body of knowledge on this promising natural compound.