Integrated multi-omics approach reveals a role of ALDH1A1 in lipid metabolism in human colon cancer cells
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PROVIDER: PRJNA495397 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7990270.fastq.gz | Fastqsanger.gz | |||
| SRR7990280.fastq.gz | Fastqsanger.gz | |||
| SRR7990290.fastq.gz | Fastqsanger.gz | |||
| SRR7990300.fastq.gz | Fastqsanger.gz | |||
| SRR7990310.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. An integrated systems approach was used with bioinformatics tools to understand the the role of aldehyde dehydrogenase 1A1 (ALDH1A1) in human colon cancer cells. We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells Methods: COLO 320 cells. Cellular ALDH1A1 expression was suppressed using MISSION shRNA lentiviral transduction particles containing validated ALDH1A1 shRNA in a pLKO plasmid vector. Scramble control shRNA in pLKO plasmid vector was used to generate control cells. Signal intensities were converted to individual base calls during a run using the system's Real Time Analysis (RTA) software. Primary analysis sample de-multiplexing and alignment to the human genome was performed using Illumina's CASAVA 1.8.2 software suite. The reads were trimmed for quality and aligned with the hg19 reference genome using TopHat2. The transcripts were assembled using Cufflinks. The assembled transcripts were used to estimate transcript abundance and differential gene expression using Cuffdiff. The results were visualized using R (CRAN) and CummeRbund Results: We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells. RNA-seq and proteomics analyses revealed 1747 transcripts and 336 proteins that were differentially expressed in cells in which the ALDH1A1 had been suppressed by shRNA transfection. Transcriptomics pathway analysis showed significant signaling pathways, such as Wnt/β-catenin (p=3.47E-6), molecular mechanisms of cancer (7.41E-5) and others relevant to lipid metabolism, such as cholesterol biosynthesis I, II and III (p=1.82E-4). Proteomics pathway analysis identified oxidative phosphorylation to be the most highly significant pathway (p=5.01E-31). Pathway analysis of the genes common to the transcriptomics and proteomics analyses revealed the asparagine biosynthesis (p=3.16E-3) and cholesterol biosynthesis the most statistically significant (p=3.36E-3). Univariate analysis of the untargeted metabolomics dataset revealed 859 ions statistically significant. Network analysis showed alterations in pathways linked to energy and lipid metabolism, such carnitine shuttle (p=5.3E-4) and fatty acid oxidation (p=2.9E-2). A systems biology approach was used to integrate all three datasets and a total of 61 pathways were generated. A direct association between the suppression of ALDH1A1 and the Vitamin A (retinol) metabolism pathway and downregulation of retinol and the UGT2B17, UGT2A3 and PRDX6 genes was shown Conclusions: the present results confirm the role of ALDH1A1 in retinol metabolism and shows for the first time the influence of this gene on lipid metabolism pathways that may be crucial for cholesterol synthesis in human colon cancer cells. In addition, they demonstrate how an integrated systems approach using unbiased bioinformatics tools can be used to understand the interplay of cellular pathways.
2019-10-08 | GSE121043 | GEO
Project description:An integrated systems approach was used with bioinformatics tools to understand the the role of aldehyde dehydrogenase 1A1 (ALDH1A1) in human colon cancer cells. We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells. RNA-seq and proteomics analyses revealed 1747 transcripts and 336 proteins that were differentially expressed in cells in which the ALDH1A1 had been suppressed by shRNA transfection. Transcriptomics pathway analysis showed significant signaling pathways, such as Wnt/β-catenin (p=3.47E-6), molecular mechanisms of cancer (7.41E-5) and others relevant to lipid metabolism, such as cholesterol biosynthesis I, II and III (p=1.82E-4). Proteomics pathway analysis identified oxidative phosphorylation to be the most highly significant pathway (p=5.01E-31). Pathway analysis of the genes common to the transcriptomics and proteomics analyses revealed the asparagine biosynthesis (p=3.16E-3) and cholesterol biosynthesis the most statistically significant (p=3.36E-3). Univariate analysis of the untargeted metabolomics dataset revealed 859 ions statistically significant. Network analysis showed alterations in pathways linked to energy and lipid metabolism, such carnitine shuttle (p=5.3E-4) and fatty acid oxidation (p=2.9E-2). A systems biology approach was used to integrate all three datasets and a total of 61 pathways were generated. A direct association between the suppression of ALDH1A1 and the Vitamin A (retinol) metabolism pathway and down-regulation of retinol and the UGT2B17, UGT2A3 and PRDX6 genes was shown. In conclusion, the present results confirm the role of ALDH1A1 in retinol metabolism and shows for the first time the influence of this gene on lipid metabolism pathways that may be crucial for cholesterol synthesis in human colon cancer cells. In addition, they demonstrate how an integrated systems approach using unbiased bioinformatics tools can be used to understand the interplay of cellular pathways.
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Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of ‘active’ chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms.
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