Dataset Information

Genome-wide strategies identify downstream target genes of connective tissue-associated transcription factors [RNA-seq]

ABSTRACT: Connective tissues support organs and play crucial roles in development, homeostasis and fibrosis, yet our understanding of their formation is still limited. To gain insight into the molecular mechanisms of connective tissue specification, we selected five zinc finger transcription factors - OSR1, OSR2, EGR1, KLF2 and KLF4 - based on their expression patterns and/or known involvement in connective tissue subtype differentiation. RNA-seq and ChIP-seq profiling revealed a set of common genes regulated by all five transcription factors, which we propose as connective tissue core expression set. This common core was enriched in genes associated with axon guidance and myofibroblast signature, including fibrosis-related genes. In addition, each transcription factor regulated a specific set of signalling molecules and extracellular matrix components. This suggests a concept whereby local molecular niches can be created via the expression of specific transcription factors impinging on the specification of local microenvironments. The regulatory network established here identifies common and distinct molecular signatures of limb connective tissue subtypes, provides novel insight into the signalling pathways governing connective tissue specification, and serves as a resource for connective tissue development.

ORGANISM(S): Gallus gallus

PROVIDER: GSE100516 | GEO | 2017/07/19

SECONDARY ACCESSION(S): PRJNA392018

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Analysis of various of up-regulated and down-regulated genes in normal oral mucosa and different stages of OSF. The report provides a data analysis methodology for identification of co-expressed gene patterns, as emerging clusters, in global transcriptome of oral mucosal pre-malignant and malignant conditions in comparison to their normal counterparts. Microarray based study of global gene expression is often used to extract molecular signatures underlying cancer progression. Such endeavors endorse self organizing map, a type of artificial neural network to analyze high dimensional pre-processed transcriptome data to segregate hotspot genes in component plane for disease subtypes. This report provides a data analysis methodology for identification of co-expressed gene patterns, as emerging clusters, in global transcriptome of oral mucosal pre-malignant and malignant conditions in comparison to their normal counterparts. Four exclusive cluster patterns, each involving 100 − 300 genes, were identified from component planes for oral study groups. Gene expression associated with each pattern belonged to 32 biological processes. The transcription and RNA processing, and angiogenesis were dominant in normal oral condition, while immunity, cellular differentiation and migration, neuron signalling, connective tissue organization, tumor suppression, and enzyme regulation showed predominance in oral squamous cell carcinoma. In dysplasia skeletal and ligament development processes were dominant while in non-dysplastic sub-mucous fibrosis cytoskeleton reorganization and neuron signalling were pre-dominant. In dysplasia an intermediate pattern with nine different dominant functional processes was identified. This analysis demonstrated utility of self organizing map to capture dominant enriched patterns as visual plots and revealed six common biological processes like transcription and RNA processing, cytoskeleton reorganization, angiogenesis, immunity, neuron signalling, and connective tissue remodeling in the pathogenesis of oral cancer. In fact it could provide an intuitive understanding of molecular course in carcinogenesis and may contribute for combinatorial biomarker discovery.

Dataset Information

Genome-wide strategies identify downstream target genes of connective tissue-associated transcription factors [RNA-seq]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets