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Next Generation Sequencing Facilitates Quantitative Analysis of osteogenic differentiation treated with metformin

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of osteogenic differentiation treated with metformin

PROVIDER: PRJNA814289 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR18280735.fastq.gz	Fastqsanger.gz
	SRR18280736.fastq.gz	Fastqsanger.gz
	SRR18280737.fastq.gz	Fastqsanger.gz
	SRR18280738.fastq.gz	Fastqsanger.gz
	SRR18280739.fastq.gz	Fastqsanger.gz

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Next Generation Sequencing Facilitates Quantitative Analysis of osteogenic differentiation treated with metformin

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to determine the differentially expressed genes of osteogenic differentiation after treatment with metformin by using NGS-derived transcriptome profiling (RNA-seq) Methods: Osteogenic mRNA profiles of MC3T3-E1 cells treated in osteogenic medium and in osteogenic medium with metformin, in triplicate, using Illumina instrument. Results: Using an optimized data analysis workflow, the data reveals 1946 up-regulated and 1544 down-regulated genes after metformin treatment. Conclusions: Our study represents the first detailed analysis of osteogenic transcriptomes after metformin treatment, with biologic replicates, generated by RNA-seq technology. We conclude that RNA-seq based transcriptome characterization would contribute the target prediction and drug discovery.

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Next Generation Sequencing Facilitates Quantitative Analysis of metformin on adipogenesis Transcriptomes

Project description:We reported the application of transcriptome sequencing in high-throughput analysis of metformin intervention in adipogenesis. By obtaining differentially expressed genes from transcriptome, we constructed the mRNA expression profile in mouse embryonic fibroblast adipose differentiation model. We found that metformin can effectively distinguish expressed, ready to express or stably inhibited genes in the process of adipose differentiation, which reflects the cell state and lineage potential. Using an optimized data analysis workflow, we identified 5362 different expression adipogenesis responsive genes and 2539 metformin responsive genes. This study provided a framework for the application of comprehensive transcriptome analysis to the network regulation mechanism of metformin inhibiting preadipocyte differentiation

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