Morphologic localization of YFP-tagged M-PAC1.

<p>Shown were confocal fluorescence micrographs of CHO cells expressing YFP-tagged M-PAC1 transiently and stably in light field, fluorescent field and their merge. It was observed that in both transient and stable expression,M-PAC1 construct was transported to the cell surface normally. Bar, 5 µm.</p

DataSheet_3_Gene expression analysis in endometriosis: Immunopathology insights, transcription factors and therapeutic targets.pdf

BackgroundEndometriosis is recognized as an estrogen-dependent inflammation disorder, estimated to affect 8%-15% of women of childbearing age. Currently, the etiology and pathogenesis of endometriosis are not completely clear. Underlying mechanism for endometriosis is still under debate and needs further exploration. The involvement of transcription factors and immune mediations may be involved in the pathophysiological process of endometriosis, but the specific mechanism remains to be explored. This study aims to investigate the underlying molecular mechanisms in endometriosis.MethodsThe gene expression profile of endometriosis was obtained from the gene expression omnibus (GEO) database. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied to the endometriosis GSE7305 datasets. Cibersort and MCP-counter were used to explore the immune response gene sets, immune response pathway, and immune environment. Differentially expressed genes (DEGs) were identified and screened. Common biological pathways were being investigated using the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Transcription factors were from The Human Transcription Factors. The least absolute shrinkage and selection operator (Lasso) model identified four differential expressions of transcription factors (AEBP1, HOXB6, KLF2, and RORB). Their diagnostic value was calculated by receiver operating characteristic (ROC) curve analysis and validated in the validation cohort (GSE11691, GSE23339). By constructing the interaction network of crucial transcription factors, weighted gene coexpression network analysis (WGCNA) was used to search for key module genes. Metascape was used for enrichment analysis of essential module genes and obtained HOXB6, KLF2. The HOXB6 and KLF2 were further verified as the only two intersection genes according to Support Vector Machine Recursive Feature Elimination (SVM-RFE) and random forest models. We constructed ceRNA (lncRNA-miRNA-mRNA) networks with four potential transcription factors. Finally, we performed molecular docking for goserelin and dienogest with four transcription factors (AEBP1, HOXB6, KLF2, and RORB) to screen potential drug targets.ResultsImmune and metabolic pathways were enriched in GSVA and GSEA. In single sample gene set enrichment analysis (ssGSEA), most immune infiltrating cells, immune response gene sets, and immune response pathways are differentially expressed between endometriosis and non-endometriosis. Twenty-seven transcription factors were screened from differentially expressed genes. Most of the twenty-seven transcription factors were correlated with immune infiltrating cells, immune response gene sets and immune response pathways. Furthermore, Adipocyte enhancer binding protein 1 (AEBP1), Homeobox B6 (HOXB6), Kruppel Like Factor 2 (KLF2) and RAR Related Orphan Receptor B (RORB) were selected out from twenty-seven transcription factors. ROC analysis showed that the four genes had a high diagnostic value for endometriosis. In addition, KLF2 and HOXB6 were found to play particularly important roles in multiple modules (String, WGCNA, SVM-RFE, random forest) on the gene interaction network. Using the ceRNA network, we found that NEAT1 may regulate the expressions of AEBP1, HOXB6 and RORB, while X Inactive Specific Transcript (XIST) may control the expressions of HOXB6, RORB and KLF2. Finally, we found that goserelin and dienogest may be potential drugs to regulate AEBP1, HOXB6, KLF2 and RORB through molecular docking.ConclusionsAEBP1, HOXB6, KLF2, and RORB may be potential biomarkers for endometriosis. Two of them, KLF2 and HOXB6, are critical molecules in the gene interaction network of endometriosis. Discovered by molecular docking, AEBP1, HOXB6, KLF2, and RORB are targets for goserelin and dienogest.</p

DataSheet_1_Gene expression analysis in endometriosis: Immunopathology insights, transcription factors and therapeutic targets.zip

BackgroundEndometriosis is recognized as an estrogen-dependent inflammation disorder, estimated to affect 8%-15% of women of childbearing age. Currently, the etiology and pathogenesis of endometriosis are not completely clear. Underlying mechanism for endometriosis is still under debate and needs further exploration. The involvement of transcription factors and immune mediations may be involved in the pathophysiological process of endometriosis, but the specific mechanism remains to be explored. This study aims to investigate the underlying molecular mechanisms in endometriosis.MethodsThe gene expression profile of endometriosis was obtained from the gene expression omnibus (GEO) database. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied to the endometriosis GSE7305 datasets. Cibersort and MCP-counter were used to explore the immune response gene sets, immune response pathway, and immune environment. Differentially expressed genes (DEGs) were identified and screened. Common biological pathways were being investigated using the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Transcription factors were from The Human Transcription Factors. The least absolute shrinkage and selection operator (Lasso) model identified four differential expressions of transcription factors (AEBP1, HOXB6, KLF2, and RORB). Their diagnostic value was calculated by receiver operating characteristic (ROC) curve analysis and validated in the validation cohort (GSE11691, GSE23339). By constructing the interaction network of crucial transcription factors, weighted gene coexpression network analysis (WGCNA) was used to search for key module genes. Metascape was used for enrichment analysis of essential module genes and obtained HOXB6, KLF2. The HOXB6 and KLF2 were further verified as the only two intersection genes according to Support Vector Machine Recursive Feature Elimination (SVM-RFE) and random forest models. We constructed ceRNA (lncRNA-miRNA-mRNA) networks with four potential transcription factors. Finally, we performed molecular docking for goserelin and dienogest with four transcription factors (AEBP1, HOXB6, KLF2, and RORB) to screen potential drug targets.ResultsImmune and metabolic pathways were enriched in GSVA and GSEA. In single sample gene set enrichment analysis (ssGSEA), most immune infiltrating cells, immune response gene sets, and immune response pathways are differentially expressed between endometriosis and non-endometriosis. Twenty-seven transcription factors were screened from differentially expressed genes. Most of the twenty-seven transcription factors were correlated with immune infiltrating cells, immune response gene sets and immune response pathways. Furthermore, Adipocyte enhancer binding protein 1 (AEBP1), Homeobox B6 (HOXB6), Kruppel Like Factor 2 (KLF2) and RAR Related Orphan Receptor B (RORB) were selected out from twenty-seven transcription factors. ROC analysis showed that the four genes had a high diagnostic value for endometriosis. In addition, KLF2 and HOXB6 were found to play particularly important roles in multiple modules (String, WGCNA, SVM-RFE, random forest) on the gene interaction network. Using the ceRNA network, we found that NEAT1 may regulate the expressions of AEBP1, HOXB6 and RORB, while X Inactive Specific Transcript (XIST) may control the expressions of HOXB6, RORB and KLF2. Finally, we found that goserelin and dienogest may be potential drugs to regulate AEBP1, HOXB6, KLF2 and RORB through molecular docking.ConclusionsAEBP1, HOXB6, KLF2, and RORB may be potential biomarkers for endometriosis. Two of them, KLF2 and HOXB6, are critical molecules in the gene interaction network of endometriosis. Discovered by molecular docking, AEBP1, HOXB6, KLF2, and RORB are targets for goserelin and dienogest.</p

DataSheet_2_Gene expression analysis in endometriosis: Immunopathology insights, transcription factors and therapeutic targets.zip

BackgroundEndometriosis is recognized as an estrogen-dependent inflammation disorder, estimated to affect 8%-15% of women of childbearing age. Currently, the etiology and pathogenesis of endometriosis are not completely clear. Underlying mechanism for endometriosis is still under debate and needs further exploration. The involvement of transcription factors and immune mediations may be involved in the pathophysiological process of endometriosis, but the specific mechanism remains to be explored. This study aims to investigate the underlying molecular mechanisms in endometriosis.MethodsThe gene expression profile of endometriosis was obtained from the gene expression omnibus (GEO) database. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied to the endometriosis GSE7305 datasets. Cibersort and MCP-counter were used to explore the immune response gene sets, immune response pathway, and immune environment. Differentially expressed genes (DEGs) were identified and screened. Common biological pathways were being investigated using the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Transcription factors were from The Human Transcription Factors. The least absolute shrinkage and selection operator (Lasso) model identified four differential expressions of transcription factors (AEBP1, HOXB6, KLF2, and RORB). Their diagnostic value was calculated by receiver operating characteristic (ROC) curve analysis and validated in the validation cohort (GSE11691, GSE23339). By constructing the interaction network of crucial transcription factors, weighted gene coexpression network analysis (WGCNA) was used to search for key module genes. Metascape was used for enrichment analysis of essential module genes and obtained HOXB6, KLF2. The HOXB6 and KLF2 were further verified as the only two intersection genes according to Support Vector Machine Recursive Feature Elimination (SVM-RFE) and random forest models. We constructed ceRNA (lncRNA-miRNA-mRNA) networks with four potential transcription factors. Finally, we performed molecular docking for goserelin and dienogest with four transcription factors (AEBP1, HOXB6, KLF2, and RORB) to screen potential drug targets.ResultsImmune and metabolic pathways were enriched in GSVA and GSEA. In single sample gene set enrichment analysis (ssGSEA), most immune infiltrating cells, immune response gene sets, and immune response pathways are differentially expressed between endometriosis and non-endometriosis. Twenty-seven transcription factors were screened from differentially expressed genes. Most of the twenty-seven transcription factors were correlated with immune infiltrating cells, immune response gene sets and immune response pathways. Furthermore, Adipocyte enhancer binding protein 1 (AEBP1), Homeobox B6 (HOXB6), Kruppel Like Factor 2 (KLF2) and RAR Related Orphan Receptor B (RORB) were selected out from twenty-seven transcription factors. ROC analysis showed that the four genes had a high diagnostic value for endometriosis. In addition, KLF2 and HOXB6 were found to play particularly important roles in multiple modules (String, WGCNA, SVM-RFE, random forest) on the gene interaction network. Using the ceRNA network, we found that NEAT1 may regulate the expressions of AEBP1, HOXB6 and RORB, while X Inactive Specific Transcript (XIST) may control the expressions of HOXB6, RORB and KLF2. Finally, we found that goserelin and dienogest may be potential drugs to regulate AEBP1, HOXB6, KLF2 and RORB through molecular docking.ConclusionsAEBP1, HOXB6, KLF2, and RORB may be potential biomarkers for endometriosis. Two of them, KLF2 and HOXB6, are critical molecules in the gene interaction network of endometriosis. Discovered by molecular docking, AEBP1, HOXB6, KLF2, and RORB are targets for goserelin and dienogest.</p

Bimolecular fluorescence complementation and immunofluorescence (A, B).

<p>Shown were fluorescence images of CHO cells expressing various receptor constructs, as indicated. The positive results of immunofluorescence using antibody against PAC1 showed that all constructs expressed PAC1 or D-PAC1. Cells transfected with alone Y/N- or Y/C- tagged receptors did not produce YFP fluorescence. YFP fluorescence could be visualized in the cells transfected with the PAC-Y/N +PAC-Y/C supporting homo-dimerization of PAC1. The cells transfected with D-PAC-Y/N +D-PAC-Y/C produced no YFP fluorescence. Absence of fluorescence supported no physical interaction between PAC1 and D-PAC1, while D-PAC-YFP was used as a positive control producing YFP fluorescence. Bar, 5 µm. The statistical analysis of the YFP fluorescence intensity (C) showed that only cells tranfected with PAC-Y/N +PAC-Y/C or D-PAC-YFP (positive control) had significantly higher YFP fluorescence intensity than the negative control (cells without transfection) (*p<0.01 vs. no transfection). Data were presented as means±S.E. of six independent experiments.</p

Oligonucleotide primers employed.

<p>Oligonucleotide primers employed.</p

The structure sketch map of PAC1 (A, B).

<p>The HSDCIF motif is indicated as red. The homology analysis between PACAP and PAC1-EC1 (C). The red region showed the HSDCIF motif and its high homologue PACAP (1–6). The construction of D-PAC1 (D). The gene coding D-PAC1 with the deletion of the HSDCIF motif was amplified using over-lap PCR (The arrows represented the primers used to amplify the genes coding D-PAC1 and PAC1.).</p

Effects of the oligopeptide HSDCIF on PAC1 BiFC (A).

<p>Shown were YFP fluorescence intensity produced by the transfection of the receptor constructs as indicated. The cells without transfection were used as negative control and the cells transfected with D-PAC-YFP as positive control. Exogenous HSDCIF decreased the YFP fluorescence intensity produced by PAC-Y/N+PAC-Y/C significantly. (Δ, p<0.01 vs. PAC-Y/N+PAC-Y/C. * p<0.01 vs. negative control). Data were presented as means±S.E. of six independent experiments. Effects of the oligopeptide HSDCIF on PAC1 saturation BRET (B). Shown were the BRET saturation curves plotted as a ratio of YFP fluorescence to Rlu luminescence that were obtained for pairs of PAC-Rluc and PAC-YFP studied with a fixed amount of donor (1.0 µg of DNA/dish) and increasing amounts of acceptor (0.3–6 µg of DNA/dish). The experiments were performed in the absence or presence of the oligopeptide HSDCIF. PAC1 produced a significant saturable exponential curve, while incubation with HSDCIF lowered the curves significantly. The data are represented as the means±S.E. of six independent experiments. Effects of the oligopeptide HSDCIF on PAC1 static BRET (C). BRET ratios for CHO cells expressing receptor constructs as indicated. The shaded area represents the nonspecific BRET signal generated between PAC-Rlu and soluble YFP protein, with BRET signals above this area considered to be significant. *,p<0.01, significantly above the background and significantly higher than negative control (PAC-Rluc). The BRET ratio in PAC1-expressing CHO cells incubated with HSDCIF was significantly lower than that in cells without treatment with HSDCIF (Δ, p<0.01 vs. PAC-Rluc/PAC-YFP). The data are presented as the means±S.E. of six independent experiments. Westernblot analysis of CHO cells expresing PAC-YFP, D-PAC-YFP, and PAC-YFP expressing cells incubated with exogenous oligopeptide HSDCIF (D). As shown, the band with the molecular weight (about 160 kD) consistent with the molecular weight of the dimer was absent in D-PAC-CHO, and the exogenous oligopeptide HSDCIF decreased the dimer amount in PAC-CHO significantly.</p

Static BRET assays (A).

<p>Shown were BRET ratios generated from CHO cells expressing Rlu-tagged receptor with YFP-tagged receptor constructs as indicated. For static BRET, a total of 1.0 ug of DNA/5×105 divided equally among the noted constructs in each condition was utilized. The shaded area represents the nonspecific BRET signal generated between PAC-Rlu and soluble YFP protein, with BRET signals above this area considered to be significant. Data were presented as means±S.E. of six independent experiments. *p<0.01, significantly above the background and significantly higher than negative control (PAC-Rluc). Saturation BRET assays (B). Shown were the BRET saturation curves plotted as ratios of YFP fluorescence to Rlu luminescence that were observed for tagged receptor constructs studied with a fixed amount of donor and increasing amounts of acceptor. PAC-Rluc/PAC-YFP receptor constructs yielded exponential curves that reached asymptotes indicating significant homo-dimerization of PAC1, while D-PAC-Rluc/D-PAC-YFP and D-PAC-Rluc/ PAC-YFP yielded curves not different from a straight line, indicating that D-PAC1 lost the ability to form dimer with itself and with PAC1. The data are represented as the means±S.E. of six independent experiments.</p

Effects of oligopeptide HSDCIF on the viability of PAC1-CHO cells as measured by MTT assay (A).

<p>Data are presented as means ±S.E. obtained from six independent experiments. *p< 0.01 HSDCIF groups vs. control group, #p< 0.01, PACAP groups vs. control group. As shown, exogenous HSDCIF had contrary effects with PACAP on the cell viability. Effects of oligopeptide HSDCIF (100 nM) on the activity of PACAP with gradient concentrations (B). Data are presented as means ±S.E. obtained from six independent experiments. $p< 0.01 HSDCIF(100 nM)+PACAP groups vs. PACAP groups. Effects of oligopeptide HSDCIF with gradient concentrations on the activity of PACAP(100 nM) (C). Data are presented as means ±S.E. obtained from six independent experiments. &p< 0.01 HSDCIF+PACAP(100 nM) groups vs. PACAP(100 nM) group. As shown, exogenous HSDCIF decreased the effects of PACAP on the cell viability significantly.</p