Börno, S. T. | bioRxiv Science

bioRxiv2017

Genome-wide strategies identify molecular niches regulated by connective tissue-associated transcription factors

BackgroundConnective tissues support, connect and separate tissues and organs, playing crucial roles in development, homeostasis and fibrosis. Cell specification and differentiation is triggered by the activity of specific transcription factors. While key transcription factors have been identified for differentiation processes of most tissues, connective tissue differentiation remains largely unstudied.\n\nResultsTo gain insight into the regulatory cascades involved in connective tissue differentiation, we selected five zinc finger transcription factors - OSR1, OSR2, EGR1, KLF2 and KLF4 - based on their expression patterns and/or known involvement in the differentiation of mesenchymal cells into connective tissue subtypes. We combined RNA-seq with ChIP-seq profiling in chick limb cells following overexpression of individual transcription factors. We identified a set of common genes regulated by all five transcription factors, which constitutes a connective tissue core expression set. This common core was enriched in genes associated with axon guidance and myofibroblast signature. In addition, each of the transcription factors regulated a different set of extracellular matrix components and signalling molecules, which define local molecular niches important for connective tissue development and function.\n\nConclusionsThe established regulatory network identifies common and distinct molecular signatures downstream of five connective tissue-associated transcription factors and provides insight into the signalling pathways governing limb connective tissue differentiation. It also suggests a concept whereby local molecular niches can be created via the expression of specific transcription factors impinging on the specification of microenvironments.

developmental biology↗

bioRxiv2017

A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.

bioinformatics↗