Find research datasets worth reusing

opennotspecifiedFeb 2022View details →

Fig. 7 in Sesquiterpenes from Kadsura coccinea attenuate rheumatoid arthritis-related inflammation by inhibiting the NF-κB and JAK2/STAT3 signal pathways

Fig. 7. Experimental validation of the representative signaling pathways in vitro. Compound 19 inhibited the NF-kB (A) and JAK2/STAT3 (B) signaling pathways in RAW 264.7 cells induced by LPS. #p <0.05 compared with control group. *p <0.05, **p <0.01 compared with model group.

opennotspecifiedFeb 2022View details →

Fig. 6 in Sesquiterpenes from Kadsura coccinea attenuate rheumatoid arthritis-related inflammation by inhibiting the NF-κB and JAK2/STAT3 signal pathways

Fig. 6. (A) The gene ontology (GO) enrichment analysis for potential targets. (B) The KEGG pathway enrichment analysis of potential targets. (C) The drugcompound-target-pathway (D-C-T-P) network established by Cytoscape V 3.6.0. The red node represents K. coccinea, the yellow represents active compounds, the blue nodes represent targets, and the green nodes represent the pathways. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedFeb 2022View details →

Fig. 5 in Sesquiterpenes from Kadsura coccinea attenuate rheumatoid arthritis-related inflammation by inhibiting the NF-κB and JAK2/STAT3 signal pathways

Fig. 5. The networks of sesquiterpenoid compounds of K. coccinea in treating rheumatoid arthritis. (A) The drug-compound-target (D-C-T) network of K. coccinea. The red node represents K. coccinea, the yellow represents active compounds, and the blue nodes represent targets. (B) The protein-protein interaction (PPI) network of protein targets obtained from the STRING database and visualized by Cytoscape V 3.6.0. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 5. Analysis of 2D Scores Plot by Partial Least Squares Discriminant Analysis (PLS-DA) of characterized flavonoids in soybean leaves from the C9 and WT genotypes, infected (I) or noninfected (NI) by P. s. pv. tomato 36 h after inoculation. Points represent replicates analyzed, whereas ellipses indicate 95% confidence region.

Fig. 9 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 9. Expression analysis of target genes performed by qRT-PCR involved in plant bacterial interactions from BiP-overexpressing (C9) and wild-type (WT) soybean plants infected (I) or noninfected (NI) with P. syringae pv. tomato. The expression levels were obtained using the 2-ΔCT method. Bars (mean SE; ± n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).

Fig. 8 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 8. Relative abundance of the flavonoid derivatives from BiP-overexpressing (C9) and wild-type (WT) soybean plants infected (I) or noninfected (NI) with P. syringae pv. tomato. Bars (mean ± SE; n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).

Fig. 4 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 4. Absolute concentrations of phytohormones (A) and of flavonoid aglycones (B) by UHPLC/MS QqQ. The data represent the mean ± standard error. Bars (mean ± SE; n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).

Fig. 3 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 3. Clustering analysis by Heat Map method of the characterized metabolites by GC/MS in soybean leaves from the WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato 36 h after inoculation. Differences in the abundance of the metabolites are indicate in response to treatments.

Fig. 6 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 6. Clustering analysis by Heat Map method of the characterized flavonoids by LC QqQ in soybean leaves from the WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato 36 h after inoculation. This shows the differences in the abundance of the flavonoids analyzed by LC-MS in response to bacterial infection. Differences in the abundances of the detected flavonoids are indicate in response to treatments. Green color represents a decrease, and red color an increase.

Fig. 7 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 7. Schematic overview of flavonoid biosynthesis pathway reconstructed using the characterized compounds from soybean leaves. Each colored square box is indicative of the abundance levels of the metabolites involved in the flavonoid biosynthesis and identified for each WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato. The main flavonoids of pathway are sketched by continuous line while compounds not detected, but intermediate of the pathway, are sketched by dashed line. Compounds marked by blue asterisk were more abundant in the inoculated C9 genotype while compounds marked by red asterisk were more abundant in the inoculated WT genotype.

Fig. 2 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways

Fig. 2. Analysis of the metabolic profiles of the C9 and WT genotypes in soybean leaves from the WT and C9 genotypes infected (I) or noninfected (NI) by P. syringae pv tomato 36 h after inoculation. In (A) 2D Scores Plot generated by Partial Least Squares Discriminant Analysis (PLS-DA) of all the metabolites. Points represent analyzed replicates, whereas ellipses indicate 95% confidence region. In (B) Major metabolites responsible for discrimination between inoculated and mock inoculated soybean groups identified by VIP score. Green color represents a decrease, and red color an increase.

Fig. 5 in Nerolidol inhibits proliferation of leiomyoma cells via reactive oxygen species-induced DNA damage and downregulation of the ATM/ Akt pathway

Fig. 5. Nerolidol suppressed the protein and mRNA expression of ATM in rat leiomyoma cells. (A, B) ELT3 cells were treated with nerolidol for 48 h and were then harvested for the western blot analysis of phosphorylated ATM and total ATM (A) and for determining ATM mRNA expression using quantitative RT-PCR (B). GAPDH was used as the loading control. The inserted numbers in (A) represent the relative expression level compared to that of the vehicle control (indicated as 0 μM). (C, D, E, F) ELT3 cells were treated with the indicated concentrations of KU-55933, an ATM inhibitor, for 48 h, following which the cells were harvested for further analysis. (C) The expression levels of p-ATMser1981, total ATM, p-Aktser473, total Akt, CDK4, and CDK6 were determined using western blot analysis. β-Actin was used as the loading control. The inserted numbers in (C) represent the relative expression level compared to that of the vehicle control (indicated as 0 μM). (D, E) Cell cycle analysis was performed after propidium iodide staining and FACS, and the results were analyzed using the FlowJo software (D). The percentages of cells in each cell cycle phase are plotted in (E). (F) Cell proliferation was determined using the MTT assay. DMSO (0.1 %) was used as the vehicle control. The data are presented as relative percentage compared to that of the vehicle control. The p-value was calculated using one-way ANOVA with Tukey's multiple post hoc test for each group. *, p <0.05; **, p <0.01, and ***, p <0.001 compared to the control group.

Fig. 3 in Nerolidol inhibits proliferation of leiomyoma cells via reactive oxygen species-induced DNA damage and downregulation of the ATM/ Akt pathway

Fig. 3. Nerolidol treatment led to the generation of intracellular ROS and impaired the mitochondrial membrane potential. (A) ELT3 cells were harvested after trypsin/EDTA treatment and then treated with 20 mM NAC, followed by DCFDA staining for 30 min. The cells were then treated with nerolidol for another additional 30 min, and DCF fluorescence was detected using flow cytometry. H2O2 was used as the positive control. The data were analyzed using the FlowJo software. (B, C) ELT3 cells were harvested and treated with nerolidol for 30 min. The cells were then stained with JC-1 dye for 15 min, followed by flow cytometry analysis. FCCP was used as the positive control. The data were analyzed using the FlowJo software. (B) The gated cells in the graphs indicate the cells with low mitochondrial membrane potential, and the quantitative results are plotted in (C). (D) ELT3 cells were treated with the indicated concentration of nerolidol for 48 h with or without co-treatment with 5 mM NAC. Cell proliferation was determined using the MTT assay. Ethanol (1 %) was used as the vehicle control. The data are presented as relative percentage compared to that of the vehicle control. (E) ELT3 cells were treated with the indicated concentration of nerolidol for 48 h with or without co-treatment with 20 μM ferrostatin-1. Cell proliferation was determined using the MTT assay. Ethanol (1 %) was used as the vehicle control. The data are presented as relative percentage compared to that of the vehicle control. The p-values were calculated using one-way ANOVA with Tukey's multiple post hoc test for each group. *, p <0.05; ***, p <0.001 compared to the vehicle control group (C). ***, p <0.001 compared to the nerolidol single treatment group (D, E).

Fig. 4 in Nerolidol inhibits proliferation of leiomyoma cells via reactive oxygen species-induced DNA damage and downregulation of the ATM/ Akt pathway

Fig. 4. Treatment with nerolidol triggered DNA damage in rat leiomyoma cells. (A, B) ELT3 cells were harvested and treated with 100 μM nerolidol for 30 min, followed by determination of DNA damage using the comet assay. H2O2 (200 μM) was used as the positive control. The tail moments are indicated by red arrows in (A), as quantified using a live video imaging system, and dot plots show the mean ± SD in (B). (C, D, E) ELT3 cells were treated with 100 μM nerolidol for 1 h, followed by incubation with anti-p-γH2AXser139 antibody and FITC-conjugated secondary antibody. Nuclei were visualized using DAPI (blue). (C) P-γH2AXser139 expression was quantified using an automated imaging system. The relative positive cell number and the nuclear p-γH2AXser139 intensities are plotted in (D) and (E), respectively. The p-value was calculated using one-way ANOVA with Tukey's multiple post hoc test for each group. *, p <0.05; **, p <0.01, and ***, p <0.001 compared to the vehicle control group (1 % EtOH). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2. Nerolidol induced G1 in Nerolidol inhibits proliferation of leiomyoma cells via reactive oxygen species-induced DNA damage and downregulation of the ATM/ Akt pathway

Fig. 2. Nerolidol induced G1 cell cycle arrest by downregulating Akt phosphorylation and cell cycle-related proteins. ELT3 cells were treated with the indicated concentrations of nerolidol for 48 h and were then harvested after trypsin/EDTA treatment. (A, B) For cell cycle analysis, the harvested cells were fixed with 70 % ethanol, followed by propidium iodide staining, and the DNA content was measured using flow cytometry. The results were analyzed using the FlowJo software (A). Blue, olive green, and green represent the G1, S, and G2/M phases, respectively. The percentage of each cell cycle phase was plotted in (B). The p-value was calculated using one-way ANOVA with Tukey's multiple post hoc test for each group.*, p <0.05; **, p <0.01, and ***, p <0.001 compared to the vehicle control group (1 % EtOH). (C) Total cellular protein content was measured, and the expression levels of phosphorylated Akt, total Akt, cyclin D1, CDK4, and CDK6 were determined using western blot analysis. β-Actin was used as the loading control. The inserted numbers in (C) indicate the relative expression level compared to that of the vehicle control (indicated as 0 μM). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Nerolidol inhibits proliferation of leiomyoma cells via reactive oxygen species-induced DNA damage and downregulation of the ATM/ Akt pathway

Fig. 1. Nerolidol inhibited the proliferation of rat leiomyoma cells. ELT3 cells were seeded in 24-well plates and treated with the indicated concentrations of nerolidol for 48 h. (A) Images were captured under an inverted microscope at 100 × magnification. (B) The cells were trypsinized, and the number of viable (upper panel) or dead (lower panel) cells was counted after staining with trypan blue with a hemocytometer. (C) Cell proliferation was also determined using the MTT assay. Ethanol (1 %) was used as the vehicle control, which is indicated as 0 μM. The data are presented as relative percentage compared to the vehicle control. The p-value was calculated using one way ANOVA with Tukey's multiple post hoc test for each group. *, p <0.05 and ***, p <0.001 compared to the vehicle control group. The IC50 values were calculated using the IC50 Calculator (https://www.aatbio.com/tools/ic50-calculator).

opencc-by-4.0Aug 2023View details →

Reward expectation enhances action-related activity of nigral dopaminergic and two striatal output pathways

Data for making main and supplementary figures from 'Reward expectation enhances action-related activity of nigral dopaminergic and two striatal output pathways'. The figure (and the corresponding subplot) is specified in each file. In the behavior folder, each row corresponds to one recording session. In the neuronal activity data, each row correspond to one neuron. In the dLight data, each row corresponds to one recording session.

opencc-by-4.0Aug 2023View details →

Proteomic detection of COX-2 pathway-related factors in patients with adenomyosis

Peptide, protein, protein_merge_info and psm  are part of mass spectrometry. HE and LE, only LE and only HE are part of bioinformatics analysis.

dryad32/100

The midnolin-proteasome pathway catches proteins for ubiquitination-independent degradation

Cells use ubiquitin to mark proteins for proteasomal degradation. While the proteasome also eliminates proteins that are not modified by ubiquitin, how this occurs mechanistically is unclear. We show here that midnolin promotes the destruction of many nuclear proteins including transcription factors encoded by the immediate-early-genes. Diverse environmental cues induce midnolin and its overexpression is sufficient to cause the degradation of its targets by a mechanism, which, remarkably, does not require ubiquitination. Instead, midnolin associates with the proteasome via an alpha-helix, employs its Catch-domain to bind a region within substrates that adopts a beta-strand conformation, and uses a ubiquitin-like-domain to promote substrate destruction. Thus, midnolin contains three regions that function in concert to target a large set of nuclear proteins to the proteasome for degradation.

opencc-zeroAug 2023View details →