growth factor beta 1  (MedChemExpress)

Journal: Aging Cell

Article Title: Characterizing the SASP ‐Dependent Paracrine Spreading of Senescence Between Human Brain Cell Types

doi: 10.1111/acel.70673

Figure Lengend Snippet: Analysis of ligands and receptors in senescent and receiving cells. (A) Schematic depicting BulkSignalR pipeline which uses known ligand‐receptor interactions and affected downstream pathways to analyze their activation based on our bulk RNAseq data from DMSO and BrdU treated human cell lines (created with BioRender). (B) Venn diagram showing the number of receptors inferred from BulkSignalR to be activated across each of the five human cell types. Three receptors were identified in common between astrocytes (purple), endothelial cells (pink), and microglia (yellow) which were the cell types shown (Figure ) to be capable of receiving senescence signals and becoming SA β‐gal positive: CXCR7, KREMEN2, and GIPR. Only CXCR7 was expressed in the cell types capable of entering secondary senescence (astrocytes, endothelial cells, microglia) (Figure , Figure ). (C) TPM expression values of CXCR7 , its ligand CXCL12 , and DPP4 which cleaves and inactivates CXCL12 in DMSO (gray) and BrdU (red) treated cell lines ( n = 3 replicates). (D) Schematic of the four selected SASP inhibitors mechanisms of action: Bindarit is a CCL2 synthesis inhibitor which prevents p65 activation of the CCL2 gene at the promoter region, ISO‐1 is a MIF antagonist, ACT‐1004‐1239 is a CXCR7 antagonist, and Sitagliptin inhibits DPP4 preventing its action of cleaving and inactivating CXCL12 (created with BioRender). Data were analyzed by two‐way ANOVA with Tukey's multiple comparisons test (C). All graphs show mean with error bars depicting standard deviation (ns, p > 0.05, ** p < 0.01, *** p < 0.001).

Article Snippet: Treatment with migration inhibitor factor (MIF) antagonist ISO‐1 (MedChemExpress, Catalog No. HY‐16692) (Kok et al. ) was utilized to prevent MIF‐associated communication between senescence inducers and receiving cells.

Techniques: Activation Assay, RNA sequencing, Expressing, Standard Deviation

Journal: Aging Cell

Article Title: Characterizing the SASP ‐Dependent Paracrine Spreading of Senescence Between Human Brain Cell Types

doi: 10.1111/acel.70673

Figure Lengend Snippet: Targeting SASP ligands and receptors to prevent the spreading of senescence. (A) Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with 100 μM BrdU (red) along with 200 μM Bindarit (pink), 50 μM ISO‐1 (blue), 200 μM ACT‐1004‐1239 (orange), or 2 μM Sitagliptin (green) ( n = 6 replicates). (B) Quantification of percentage of SA β‐gal positive microglia following 7‐day treatment with 100 μM BrdU (red) along with 200 μM Bindarit (pink), 50 μM ISO‐1 (blue), 200 μM ACT‐1004‐1239 (orange), or 2 μM Sitagliptin (green) ( n = 6 replicates). (C) Timeline showing treatment with DMSO + Bindarit CM or BrdU + Bindarit CM for 7 days. Timeline showing treatment with DMSO or 100 μM BrdU along with SASP inhibitors (ISO‐1, ACT‐1004‐1239, or Sitagliptin) for 7 days. Features of senescence were analyzed 8 days after the initial plating of cells (created with BioRender). (D) Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with DMSO + Bindarit CM from astrocytes (gray), BrdU + Bindarit CM from astrocytes (red), DMSO CM from astrocytes + SASP inhibitor (gray), or BrdU CM from astrocytes + SASP inhibitor (red) ( n = 4 replicates). Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with DMSO CM from astrocytes + Bindarit (gray) or BrdU CM from astrocytes + Bindarit (red) ( n = 4 replicates). (E) Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with DMSO + Bindarit CM from microglia (gray), BrdU + Bindarit CM from microglia (red), DMSO CM from microglia + SASP inhibitor (gray), or BrdU CM from microglia + SASP inhibitor (red) ( n = 4 replicates). Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with DMSO CM from microglia + Bindarit (gray) or BrdU CM from microglia + Bindarit (red) ( n = 4 replicates). (F) Quantification of percentage of SA β‐gal positive microglia following 7‐day treatment with DMSO + Bindarit CM from microglia (gray), BrdU + Bindarit CM from microglia (red), DMSO CM from microglia + SASP inhibitor (gray), or BrdU CM from microglia + SASP inhibitor (red) ( n = 4 replicates). Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with DMSO CM from microglia + Bindarit (gray) or BrdU CM from microglia + Bindarit (red) ( n = 4 replicates). (G) Quantification of percentage of SA β‐gal positive microglia following 7‐day treatment with DMSO + Bindarit CM from astrocytes (gray), BrdU + Bindarit CM from astrocytes (red), DMSO CM from astrocytes + SASP inhibitor (gray), or BrdU CM from astrocytes + SASP inhibitor (red) ( n = 4 replicates). Quantification of percentage of SA β‐gal positive astrocytes following 7‐day treatment with DMSO CM from astrocytes + Bindarit (gray) or BrdU CM from astrocytes + Bindarit (red) ( n = 4 replicates). Data analyzed by unpaired t ‐test (A, B) and two‐way ANOVA with Tukey's or Šídák's multiple comparisons test (D–G). All graphs show mean with error bars depicting standard deviation (ns, p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001).

Article Snippet: Treatment with migration inhibitor factor (MIF) antagonist ISO‐1 (MedChemExpress, Catalog No. HY‐16692) (Kok et al. ) was utilized to prevent MIF‐associated communication between senescence inducers and receiving cells.

Techniques: Standard Deviation

Journal: Bioactive Materials

Article Title: Mesenchymal stromal cells-loaded 3D radially aligned composite scaffold with potentiated paracrine signaling for sequential bone regeneration

doi: 10.1016/j.bioactmat.2026.02.059

Figure Lengend Snippet: Temporal analysis of the BMSC paracrine profile on different scaffolds. (A) Confocal microscopy images from Live/Dead fluorescence staining of BMSCs encapsulated within the PCL/HAp-GelMA/BMSCs scaffold after 1, 3, 5, and 14 d of 3D culture (live cells, green; dead cells, red). (B) The concentrations of key paracrine factors (TGF-β, PGE2, VEGF, HGF, and BMP-2) from BMSCs cultured in different scaffolds, quantified from culture supernatants at day 3 and day 7. (C) Corresponding relative mRNA expression levels of TGFB1, PTGS2, VEGFA, HGF, and BMP-2 in BMSCs at day 3 and day 7, as determined by qPCR analysis. Data are presented as mean ± SD (n = 3) *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; ns: not significant.

Article Snippet: ELISA kits for PGE2 (Cat. No. E-EL-0034), TGF-β (Cat. No. E-EL-0162), VEGF (Cat. No. E-EL-R2603), and HGF (Cat. No. E-EL-R0496) were purchased from Elabscience (Wuhan, China).

Techniques: Confocal Microscopy, Fluorescence, Staining, Cell Culture, Expressing

Journal: International Journal of Molecular Medicine

Article Title: Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning

doi: 10.3892/ijmm.2026.5869

Figure Lengend Snippet: Hub genes for DKD diagnosis. (A) Protein-protein interaction network of intersecting genes. Diagnostic markers identified by the (B) closeness, (C) degree and (D) MCC algorithms via the cytoHubba plugin. (E and F) Screening of diagnostic markers using the LASSO logistic regression algorithm. (G) Biomarker screening using the SVM-RFE method. (H) Biomarker screening based on the RF algorithm. (I and J) UpSet plots and petal diagrams showing intersecting diagnostic markers identified by all six algorithms. (K) Nomogram for predicting DKD. (L) Decision curve analysis curve for validation of diagnostic efficacy. (M) ROC curve for validation of diagnostic efficacy of key genes. Violin plots comparing (N) SYK expression levels, (O) ADAM10 expression levels and (P) APAF1 expression levels in DKD vs. control samples within the GSE30122 validation cohort. * P<0.05, *** P<0.001. Analysis of the relationships between (Q) SYK and serum creatinine, (R) ADAM10 and serum creatinine, (S) APAF1 and serum creatinine, (T) SYK and GFR, (U) ADAM10 and GFR, and (V) APAF1 and GFR. ADAM10, ADAM metallopeptidase domain 10; APAF1, apoptotic peptidase activating factor 1; AUC, area under the ROC curve; DKD, diabetic kidney disease; FPR, false positive rate; GFR, glomerular filtration rate; LASSO, least absolute shrinkage and selection operator; MCC, maximum clique centrality; ROC, receiver operating characteristic; RF, random forest; SVM-RFE, support vector machine-recursive feature elimination; SYK, spleen-associated tyrosine kinase; TPR, true positive rate.

Article Snippet: Cells were transfected with siRNAs targeting apoptotic peptidase activating factor 1 (APAF1; cat. no. HY-RS00801), ADAM metallopeptidase domain 10 (ADAM10; cat. no. HY-RS00265) and spleen-associated tyrosine kinase (SYK; cat. no. HY-RS14075) (all from MedChemExpress).

Techniques: Biomarker Discovery, Diagnostic Assay, Expressing, Control, Filtration, Selection, Plasmid Preparation

Journal: International Journal of Molecular Medicine

Article Title: Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning

doi: 10.3892/ijmm.2026.5869

Figure Lengend Snippet: Analysis of the immune microenvironment. (A) Principal component analysis clustering plot illustrating immune cell infiltration patterns. The diagram presents differences in immune profiles between the DKD group and controls. (B) A heatmap displaying immune cell type correlations, where red signifies positive and blue negative associations, and the color saturation corresponds to the strength of the correlation. (C) Analysis illustrating disparities in immune cell infiltration between DKD and controls kidney tissue samples. * P<0.05, *** P<0.001. (D) Relative proportions of 22 immune cell subpopulations. Correlations between (E) ADAM10 and immune cells, (F) APAF1 and immune cells, and (G) SYK and immune cells. Point size corresponds to correlation strength between the hub gene and different immune cells; stronger correlations are shown by larger points, while weaker ones are shown by smaller points. ADAM10, ADAM metallopeptidase domain 10; APAF1, apoptotic peptidase activating factor 1; DKD, diabetic kidney disease; SYK, spleen-associated tyrosine kinase.

Article Snippet: Cells were transfected with siRNAs targeting apoptotic peptidase activating factor 1 (APAF1; cat. no. HY-RS00801), ADAM metallopeptidase domain 10 (ADAM10; cat. no. HY-RS00265) and spleen-associated tyrosine kinase (SYK; cat. no. HY-RS14075) (all from MedChemExpress).

Techniques:

Journal: International Journal of Molecular Medicine

Article Title: Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning

doi: 10.3892/ijmm.2026.5869

Figure Lengend Snippet: Validation of pivotal gene expression in the mouse DKD model. (A) Schematic illustration of the mouse DKD model. (B) Quantification of UACR in mice. (C) Scr and (D) BUN levels in the two experimental groups. (E) Body weight changes in mice over time. (F) Blood glucose alterations in mice. (G) H&E staining of mouse kidney tissue sections, highlighting the extent of renal injury. Renal fibrosis was assessed using Masson's trichrome staining (magnification, ×200; scale bar, 100 µ m). The arrows point to dilated renal tubules and atrophic glomeruli. Semi-quantitative analysis of (H) H&E and (I) Masson staining results, with error bars in (H) representing the 95% confidence interval for the median. Reverse transcription-quantitative PCR detection of (J) APAF1, (K) SYK and (L) ADAM10 mRNA expression. (M) Western blot analysis showing the expression levels of SYK, ADAM10 and APAF1 in the UNx/STZ/HFD model, and (N) semi-quantitative analysis. (O) Western blot analysis showing the expression levels of SYK, ADAM10 and APAF1 in the db/db model, and (P) semi-quantitative analysis. Data are presented as the mean ± SD or median (n=6). * P<0.05, ** P<0.01, *** P<0.001. ADAM10, ADAM metallopeptidase domain 10; APAF1, apoptotic peptidase activating factor 1; BUN, blood urea nitrogen; DKD, diabetic kidney disease; H&E, hematoxylin and eosin; HFD, high-fat diet; Scr, serum creatinine; STZ, streptozotocin; SYK, spleen-associated tyrosine kinase; UACR, urine albumin-to-creatinine ratio; UNx, uninephrectomy.

Article Snippet: Cells were transfected with siRNAs targeting apoptotic peptidase activating factor 1 (APAF1; cat. no. HY-RS00801), ADAM metallopeptidase domain 10 (ADAM10; cat. no. HY-RS00265) and spleen-associated tyrosine kinase (SYK; cat. no. HY-RS14075) (all from MedChemExpress).

Techniques: Biomarker Discovery, Gene Expression, Staining, Reverse Transcription, Real-time Polymerase Chain Reaction, Expressing, Western Blot

Journal: International Journal of Molecular Medicine

Article Title: Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning

doi: 10.3892/ijmm.2026.5869

Figure Lengend Snippet: siRNA-mediated silencing of pivotal genes can alleviate fibrosis. (A) SYK siRNA reduced SYK mRNA levels. (B) Western blot analysis showing the expression levels of SYK, fibronectin, vimentin and Snail, and (C) semi-quantitative analysis. (D) ADAM10 siRNA reduced ADAM10 mRNA levels. (E) Western blot analysis showing the expression levels of ADAM10, fibronectin, vimentin and Snail, and (F) semi-quantitative analysis. (G) APAF1 siRNA reduced APAF1 mRNA levels. (H) Western blot analysis showing the expression levels of APAF1, fibronectin, vimentin and Snail, and (I) semi-quantitative analysis. Data are presented as the mean ± SD (n=6). * P<0.05, ** P<0.01, *** P<0.001. ADAM10, ADAM metallopeptidase domain 10; APAF1, apoptotic peptidase activating factor 1; Con, control; HG, high glucose; LG, low glucose; ns, not significant; siRNA, small interfering RNA; SYK, spleen-associated tyrosine kinase.

Article Snippet: Cells were transfected with siRNAs targeting apoptotic peptidase activating factor 1 (APAF1; cat. no. HY-RS00801), ADAM metallopeptidase domain 10 (ADAM10; cat. no. HY-RS00265) and spleen-associated tyrosine kinase (SYK; cat. no. HY-RS14075) (all from MedChemExpress).

Techniques: Western Blot, Expressing, Control, Small Interfering RNA

Journal: International Journal of Molecular Medicine

Article Title: Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning

doi: 10.3892/ijmm.2026.5869

Figure Lengend Snippet: Clinical validation of hub genes. Relative mRNA levels of (A) SYK, (B) ADAM10 and (C) APAF1 in non-DKD patients and patients with DKD. Data are presented as the mean ± SD (n=15). * P<0.05, ** P<0.01, *** P<0.001. ADAM metallopeptidase domain 10; APAF1, apoptotic peptidase activating factor 1; DKD, diabetic kidney disease; SYK, spleen-associated tyrosine kinase.

Article Snippet: Cells were transfected with siRNAs targeting apoptotic peptidase activating factor 1 (APAF1; cat. no. HY-RS00801), ADAM metallopeptidase domain 10 (ADAM10; cat. no. HY-RS00265) and spleen-associated tyrosine kinase (SYK; cat. no. HY-RS14075) (all from MedChemExpress).

Techniques: Biomarker Discovery