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TargetMol t6496
T6496, supplied by TargetMol, used in various techniques. Bioz Stars score: 93/100, based on 4 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Fig. 4. <t>Vactosertib-induced</t> Ara-C resistance phenotype is not mediated through the TGF-β signaling pathway. (A-D) XTT cytotoxicity assays demonstrating the effects of TGF-β1 signaling pathway component knockdowns on Ara-C response: (A) TGFBR1, (B) ACVR1B, (C) double knockdown of TGFBR1 and ACVR1B, and (D) SMAD4. Knockdown efficiency for each target gene was validated by qPCR, with results shown in the inset graphs (n = 3, two-tailed t-test). (E) Schematic repre sentation of the RNA sequencing experimental design. (F) Volcano plot depicting significantly deregulated genes (red: upregulated, blue: downregulated) in THP1 cells upon treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively) (p-value < 0.05, | log fold change| > 1.5). (G) Heatmap illustrating the expression of negative feedback regulators of TGF-β signaling including SMAD6, SMAD7, TGFBR1, SKI, SMURF, and SPP1 in THP1, HL60, and MV4-11 cells following treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively). All p-values were less than 0.05 (two-tailed t-test). Color intensity values correspond to fold change. p-value significance is represented * ** , < 0.001.
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Fig. 4. <t>Vactosertib-induced</t> Ara-C resistance phenotype is not mediated through the TGF-β signaling pathway. (A-D) XTT cytotoxicity assays demonstrating the effects of TGF-β1 signaling pathway component knockdowns on Ara-C response: (A) TGFBR1, (B) ACVR1B, (C) double knockdown of TGFBR1 and ACVR1B, and (D) SMAD4. Knockdown efficiency for each target gene was validated by qPCR, with results shown in the inset graphs (n = 3, two-tailed t-test). (E) Schematic repre sentation of the RNA sequencing experimental design. (F) Volcano plot depicting significantly deregulated genes (red: upregulated, blue: downregulated) in THP1 cells upon treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively) (p-value < 0.05, | log fold change| > 1.5). (G) Heatmap illustrating the expression of negative feedback regulators of TGF-β signaling including SMAD6, SMAD7, TGFBR1, SKI, SMURF, and SPP1 in THP1, HL60, and MV4-11 cells following treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively). All p-values were less than 0.05 (two-tailed t-test). Color intensity values correspond to fold change. p-value significance is represented * ** , < 0.001.
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Fig. 4. <t>Vactosertib-induced</t> Ara-C resistance phenotype is not mediated through the TGF-β signaling pathway. (A-D) XTT cytotoxicity assays demonstrating the effects of TGF-β1 signaling pathway component knockdowns on Ara-C response: (A) TGFBR1, (B) ACVR1B, (C) double knockdown of TGFBR1 and ACVR1B, and (D) SMAD4. Knockdown efficiency for each target gene was validated by qPCR, with results shown in the inset graphs (n = 3, two-tailed t-test). (E) Schematic repre sentation of the RNA sequencing experimental design. (F) Volcano plot depicting significantly deregulated genes (red: upregulated, blue: downregulated) in THP1 cells upon treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively) (p-value < 0.05, | log fold change| > 1.5). (G) Heatmap illustrating the expression of negative feedback regulators of TGF-β signaling including SMAD6, SMAD7, TGFBR1, SKI, SMURF, and SPP1 in THP1, HL60, and MV4-11 cells following treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively). All p-values were less than 0.05 (two-tailed t-test). Color intensity values correspond to fold change. p-value significance is represented * ** , < 0.001.
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Fig. 4. Vactosertib-induced Ara-C resistance phenotype is not mediated through the TGF-β signaling pathway. (A-D) XTT cytotoxicity assays demonstrating the effects of TGF-β1 signaling pathway component knockdowns on Ara-C response: (A) TGFBR1, (B) ACVR1B, (C) double knockdown of TGFBR1 and ACVR1B, and (D) SMAD4. Knockdown efficiency for each target gene was validated by qPCR, with results shown in the inset graphs (n = 3, two-tailed t-test). (E) Schematic repre sentation of the RNA sequencing experimental design. (F) Volcano plot depicting significantly deregulated genes (red: upregulated, blue: downregulated) in THP1 cells upon treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively) (p-value < 0.05, | log fold change| > 1.5). (G) Heatmap illustrating the expression of negative feedback regulators of TGF-β signaling including SMAD6, SMAD7, TGFBR1, SKI, SMURF, and SPP1 in THP1, HL60, and MV4-11 cells following treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively). All p-values were less than 0.05 (two-tailed t-test). Color intensity values correspond to fold change. p-value significance is represented * ** , < 0.001.

Journal: Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy

Article Title: A quiescence-like/TGF-β1-specific CRISPRi screen reveals drug uptake transporters as secondary targets of kinase inhibitors in AML.

doi: 10.1016/j.drup.2025.101242

Figure Lengend Snippet: Fig. 4. Vactosertib-induced Ara-C resistance phenotype is not mediated through the TGF-β signaling pathway. (A-D) XTT cytotoxicity assays demonstrating the effects of TGF-β1 signaling pathway component knockdowns on Ara-C response: (A) TGFBR1, (B) ACVR1B, (C) double knockdown of TGFBR1 and ACVR1B, and (D) SMAD4. Knockdown efficiency for each target gene was validated by qPCR, with results shown in the inset graphs (n = 3, two-tailed t-test). (E) Schematic repre sentation of the RNA sequencing experimental design. (F) Volcano plot depicting significantly deregulated genes (red: upregulated, blue: downregulated) in THP1 cells upon treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively) (p-value < 0.05, | log fold change| > 1.5). (G) Heatmap illustrating the expression of negative feedback regulators of TGF-β signaling including SMAD6, SMAD7, TGFBR1, SKI, SMURF, and SPP1 in THP1, HL60, and MV4-11 cells following treatment with TGF-β1 (10 ng/mL), vactosertib (10 μM), or a combination of TGF-β1 + vactosertib (10 ng/mL and 10 μM, respectively). All p-values were less than 0.05 (two-tailed t-test). Color intensity values correspond to fold change. p-value significance is represented * ** , < 0.001.

Article Snippet: For combinatorial treatments, cells were pre-incubated with 10 ng/mL TGF-β1 (Peprotech), 10 μM vactosertib (Selleckchem), or a combination of both for 2 hours before Ara-C addition (ranging from 0 to 20 μM).

Techniques: Knockdown, Two Tailed Test, RNA Sequencing, Expressing

Fig. 5. CRISPRi screening reveals a potential link between vactosertib-induced resistance to Ara-C and alterations in drug uptake mechanisms in AML cells. (A) Schematic of the CRISPRi library screen workflow. THP1-dCas9-KRAB cells were transduced with CRISPRi library at an MOI of approximately 0.3 and selected with puromycin. Cells were treated with either Ara-C alone or in combination with vactosertib for 48 hours and were maintained for 21 days in culture. sgRNA sequences were amplified from extracted DNA and analyzed by NGS. MAGeCK was used for data processing. (B) and (C) Volcano plots indicating sgRNA distribution in vactosertib + Ara-C and Ara-C alone conditions, respectively. Data represent three independent biological replicates (n = 3). For every gene, the x-axis displays its enrichment (positive hits) or depletion (negative hits) after treatment, while the y-axis shows the score as calculated by MAGeCK. Relevant hits were annotated. (D) Validation of CRISPRi library screen results by cell growth competition assay. THP1-dCas9-KRAB cells were transduced with sgENT1 (two independent sgRNAs: sgENT1#1 and sgENT1#2), sgDCK, sgNT and sgMYBL2. The effect of each sgRNA on cell proliferation under different treatment conditions with vactosertib and Ara- C was determined by tracking the percentage of BFP-positive cells over time using flow cytometry. Results are shown as the percentage of BFP-positive cells at the indicated times relative to day 0 (n = 3).

Journal: Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy

Article Title: A quiescence-like/TGF-β1-specific CRISPRi screen reveals drug uptake transporters as secondary targets of kinase inhibitors in AML.

doi: 10.1016/j.drup.2025.101242

Figure Lengend Snippet: Fig. 5. CRISPRi screening reveals a potential link between vactosertib-induced resistance to Ara-C and alterations in drug uptake mechanisms in AML cells. (A) Schematic of the CRISPRi library screen workflow. THP1-dCas9-KRAB cells were transduced with CRISPRi library at an MOI of approximately 0.3 and selected with puromycin. Cells were treated with either Ara-C alone or in combination with vactosertib for 48 hours and were maintained for 21 days in culture. sgRNA sequences were amplified from extracted DNA and analyzed by NGS. MAGeCK was used for data processing. (B) and (C) Volcano plots indicating sgRNA distribution in vactosertib + Ara-C and Ara-C alone conditions, respectively. Data represent three independent biological replicates (n = 3). For every gene, the x-axis displays its enrichment (positive hits) or depletion (negative hits) after treatment, while the y-axis shows the score as calculated by MAGeCK. Relevant hits were annotated. (D) Validation of CRISPRi library screen results by cell growth competition assay. THP1-dCas9-KRAB cells were transduced with sgENT1 (two independent sgRNAs: sgENT1#1 and sgENT1#2), sgDCK, sgNT and sgMYBL2. The effect of each sgRNA on cell proliferation under different treatment conditions with vactosertib and Ara- C was determined by tracking the percentage of BFP-positive cells over time using flow cytometry. Results are shown as the percentage of BFP-positive cells at the indicated times relative to day 0 (n = 3).

Article Snippet: For combinatorial treatments, cells were pre-incubated with 10 ng/mL TGF-β1 (Peprotech), 10 μM vactosertib (Selleckchem), or a combination of both for 2 hours before Ara-C addition (ranging from 0 to 20 μM).

Techniques: Transduction, Amplification, Biomarker Discovery, Competitive Binding Assay, Flow Cytometry

Fig. 6. Vactosertib and other kinase inhibitors influence Ara-C transport in AML cells. (A) Schematic representation of the intracellular 13C3-Ara-C measurement. THP1 cells were pre-treated with different inhibitors for 2 hours, followed by incubation with 80 µM 13C3-Ara-C for 30 minutes. Cellular metabolites were then extracted and quantified using LC-MS/MS analysis. (B) Relative 13C3-Ara-C levels following treatment with 10 µM vactosertib, 10 µM galunisertib, or ENT1-inhibitor NBMPR at low (100 nM) and high (100 μM) concentrations. All values were normalized to the control condition of 13C3-Ara-C treatment alone (n = 5, two-tailed t- test). (C) 3D-modeling of ENT1 protein structure and small-molecule binding prediction. The binding sites of NBMPR (6OB6, left) and dilazep (6OB7, middle) were shown alongside the in silico predicted binding site of vactosertib (right), illustrating their interaction with the ENT1 transporter. Residue 151 was highlighted in red as sticks. (D) Relative 13C3-Ara-C uptake following treatment with 10 µM midostaurin, gilteritinib, ruxolitinib, and glasdegib. All values were normalized to the control condition of 13C3-Ara-C treatment alone (n = 5, two-tailed t-test). p-value significance is represented by * *, < 0.01; * ** , < 0.001.

Journal: Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy

Article Title: A quiescence-like/TGF-β1-specific CRISPRi screen reveals drug uptake transporters as secondary targets of kinase inhibitors in AML.

doi: 10.1016/j.drup.2025.101242

Figure Lengend Snippet: Fig. 6. Vactosertib and other kinase inhibitors influence Ara-C transport in AML cells. (A) Schematic representation of the intracellular 13C3-Ara-C measurement. THP1 cells were pre-treated with different inhibitors for 2 hours, followed by incubation with 80 µM 13C3-Ara-C for 30 minutes. Cellular metabolites were then extracted and quantified using LC-MS/MS analysis. (B) Relative 13C3-Ara-C levels following treatment with 10 µM vactosertib, 10 µM galunisertib, or ENT1-inhibitor NBMPR at low (100 nM) and high (100 μM) concentrations. All values were normalized to the control condition of 13C3-Ara-C treatment alone (n = 5, two-tailed t- test). (C) 3D-modeling of ENT1 protein structure and small-molecule binding prediction. The binding sites of NBMPR (6OB6, left) and dilazep (6OB7, middle) were shown alongside the in silico predicted binding site of vactosertib (right), illustrating their interaction with the ENT1 transporter. Residue 151 was highlighted in red as sticks. (D) Relative 13C3-Ara-C uptake following treatment with 10 µM midostaurin, gilteritinib, ruxolitinib, and glasdegib. All values were normalized to the control condition of 13C3-Ara-C treatment alone (n = 5, two-tailed t-test). p-value significance is represented by * *, < 0.01; * ** , < 0.001.

Article Snippet: For combinatorial treatments, cells were pre-incubated with 10 ng/mL TGF-β1 (Peprotech), 10 μM vactosertib (Selleckchem), or a combination of both for 2 hours before Ara-C addition (ranging from 0 to 20 μM).

Techniques: Incubation, Liquid Chromatography with Mass Spectroscopy, Control, Two Tailed Test, Binding Assay, In Silico, Residue