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Verteporfin treatment controls morphology, phenotype, and global gene expression for cells of the human nucleus pulposus

ABSTRACT: Purpose: It has been observed that the drug verteporfin (VP), a known disruptor of YAP-TEAD signaling, causes cell rounding and changes in phenotype and biosynthesis in human nucleus pulposus cells. In this study the time-dependent effects of VP treatment on the transcriptome of human NP cells was explored. Methods: mRNA profiles of NP cells treated for 24 hours, 48 hours, or 4 days with VP or vehicle (DMSO) control were generated for cells from 3 adult human tissue samples, using Illumina NovaSeq. Unaligned reads were trimmed based on quality score (minimum quality level (Phred) = 20, minimum read length = 25) and aligned to the whole human genome (STAR 2.6.1d; hg19) using Partek Flow software. This software suite was also used to calculate the counts/normalized counts of genes and to perform gene specific analysis (GSA), principle component analysis (PCA), hierarchical clustering, and gene set enrichment. Differentially regulated genes were considered to be those with a fold change value (VP/DMSO) for a given time point that was greater than or equal to 2 or less than or equal to negative 2 and a FDR-adjusted p-value of less than or equal to 0.05. Results: Using a data analysis workflow buit in Partek Flow, we identified 1810 differentially regulated genes at 24 hours, 4287 differentially regulated genes at 48 hours, and 9272 differentially regulated genes at 4 days. Hierarchical clustering and PCA analysis demonstrated clustering of the data by patient and time point, but separation by treatment group. We further observed that while generally the expression of fibroblastic markers decreased with VP treatment, a number of markers of NP phenotype were increased. We also identified enriched gene sets (ex. 2026 at the 4 day time point) which represented a variety of cellular components, processes, and functions potentially implicated by VP treatment. Conclusions: The data from this RNA-sequencing demonstrate that VP treatment which induces cell rounding in NP cells was also accompanied by pronounced and time-dependent changes in the transcriptome of adult NP cells in culture. The enriched gene sets and differentially regulated genes suggest a major role for cell adhesion, cytoskeletal remodeling, vacuolar lumen, and M APK activity in the NP phenotypic and functional response to changes in cell shape.

ORGANISM(S): Homo sapiens

PROVIDER: GSE151090 | GEO | 2020/08/03

REPOSITORIES: GEO

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Project description:Purpose: Degeneration and aging of the nucleus pulposus (NP) of the intervertebral disc (IVD) is accompanied by alterations in NP cell phenotype marked by reduced cellularity and a shift towards a fibroblast-like state. We have recently demonstrated an ability to manipulate the phenotypic expression of adult degenerative NP cells by culture upon poly(ethylene glycol) (PEG) based hydrogels dually functionalized with integrin- and syndecan-binding laminin-mimetic peptides. In the present study, we seek to understand the transcriptome changes elicited by NP cell interactions with the presented hydrogel system. Methods: mRNA profiles of NP from 3 human tissue samples were cultured for 4 days on either tissue culture polysyrene (TCPS) or the functionalized gel before RNA was isolated and sequenced, using Illumina NovaSeq. Unaligned reads were trimmed based on quality score (minimum quality level (Phred) = 20, minimum read length = 25) and aligned to the whole human genome (STAR 2.6.1d; hg19) using Partek Flow software. This software suite was also used to calculate the counts/normalized counts of genes and to perform gene specific analysis (GSA), principle component analysis (PCA), hierarchical clustering, and gene set enrichment. Differentially regulated genes were considered to be those with a fold change value (gel/TCPS) that was greater than or equal to 2 or less than or equal to negative 2 and a p-value of less than or equal to 0.05. Results: The data corroborate and expand on the previous findings by demonstrating that degenerative adult human NP cells cultured upon these gels have upregulations in some markers of NP and notochordal cells but also downregulations of some NP-specific markers and several fibroblastic markers. Furthermore, through gene set enrichment analysis we have shown that pathways related to cell differentiation and notochord morphogenesis were upregulated in the gel condition. Additionally, 13 genes associated with G protein-coupled receptors, many of which are known drug targets, were identified as up or downregulated following periods of culture upon the gel condition. Conclusions: The data from this RNA-sequencing demonstrate that culture on laminin-mimetic-peptide presenting gels can modulate cell phenotype and promotes upregulation of NP and notochordal markers.

Project description:Purpose: The goals of this study are to compare VEGF-treated HUVECs with or without Verteporfin (VP) pretreatment transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: After serum-starving for 12 hours, HUVECs were divided into two group: VEGF and VEGF+VP. Cells from VEGF+VP group were pretreated with VP (4 μM) for 2 hours and then all cells were treated with VEGF (200 ng/mL) for another 24 hours. Subsequently, total RNA from HUVECs were prepared using Trizol reagent and mRNA library was constructed. RNA-sequencing: RNA-sequencing was carried out by BGI (Beijing Genomic Institute, ShenZhen, China). SOAPnuke software (v1.5.2) was used to filter the data for RNA-sequencing and then these data were mapped to the reference genome using HISAT2 software (v2.0.4). The clean reads were aligned to the gene set by Bowtie2 (v2.2.5). The expression level of genes was then measured by RSEM software (v1.2.12). The heatmap of top 40 different expression genes was drawn according to the gene expression with FPKM (fragment per kilobase of transcript per million). Reactome (https://www.reactome.org/) enrichment analysis of different expression genes was undertaken and the significant levels of terms and pathways were corrected by Q value. Results: The statistical results of significant DEGS confirmed that approximately 7204 genes of the transcripts showed differential expression between the VEGF group and VEGF+VP group, with a fold change ≥1.5 and p value <0.05. Altered expression of 20 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to angiogenesis. Data analysis with GO analysis and GSEA analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed transcriptomic analysis of VEGF treated HUVECs with or without VP treatment, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles.

Project description:We have completed the Human lncRNA microarray analysis of the 4 samples that you submitted. Total RNA from each sample was quantified using the Nanodrop 1000 and the RNA integrity was assessed by Agilent 2100 Bioanalyzer. About 5 μg total RNA of each sample was used for labeling and array hybridization as the following steps: 1) Reverse transcription with invitrogen Superscript ds-cDNA synthesis kit; 2) ds-cDNA labeling with Nimblegen one-color DNA labeling kit; 3) Array hybridization using the NimbleGen Hybridization System and followed by washing with the Nimblegen wash buffer kit. 4) Array scanning using the Axon GenePix 4000B microarray scanner (Molecular Devices Corporation). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and mRNA level (*_RMA.calls) files were generated after normalization. The 4 mRNA level files were imported into Agilent GeneSpring Software (version 11.0) for further analysis. lncRNAs and mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 (“All Targets Value”) were chosen for data analysis. Differentially expressed lncRNAs and mRNAs were identified through Fold Change filtering. Pathway Analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show distinguishable lncRNA and mRNA expression profiling among samples.  Array Information: Human 4x44K lncRNA expression array information. Data Analysis for lncRNAs. Data Analysis for lncRNAs. Data Analysis for mRNAs. 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. 1. Raw lncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity lncRNAs were filtered. (lncRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis). 2. Quality assessment of lncRNA data after filtering: Contains Box Plot and Scatter Plot for lncRNAs after filtering. 3. Differentially expressed lncRNAs screening: Contains significant differentially expressed lncRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the lncRNAs after filtering.  Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. 1. Raw lncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity lncRNAs were filtered. (lncRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis). 2. Quality assessment of lncRNA data after filtering: Contains Box Plot and Scatter Plot for lncRNAs after filtering. 3. Differentially expressed lncRNAs screening: Contains significant differentially expressed lncRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the lncRNAs after filtering.  Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in RMA method by NimbleScan v2.5, and low intensity mRNAs were filtered. (mRNAs that at least 2 out of 4 samples have values greater than or equal to lower cut-off: 50.0 were chosen for further analysis) 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering. 3. Differentially expressed mRNAs screening: Contains significant differentially expressed mRNAs that passed Fold Change filtering (Fold Change >= 2.0). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of the mRNAs after filtering. 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs.

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Verteporfin treatment controls morphology, phenotype, and global gene expression for cells of the human nucleus pulposus

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