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The RNA export factor, Nxt1, is required for tissue specific transcriptional regulation.

ABSTRACT: The highly conserved, Nxf/Nxt (TAP/p15) RNA nuclear export pathway is important for export of most mRNAs from the nucleus, by interacting with mRNAs and promoting their passage through nuclear pores. Nxt1 is essential for viability; using a partial loss of function allele, we reveal a role for this gene in tissue specific transcription. We show that many Drosophila melanogaster testis-specific mRNAs require Nxt1 for their accumulation. The transcripts that require Nxt1 also depend on a testis-specific transcription complex, tMAC. We show that loss of Nxt1 leads to reduced transcription of tMAC targets. A reporter transcript from a tMAC-dependent promoter is under-expressed in Nxt1 mutants, however the same transcript accumulates in mutants if driven by a tMAC-independent promoter. Thus, in Drosophila primary spermatocytes, the transcription factor used to activate expression of a transcript, rather than the RNA sequence itself or the core transcription machinery, determines whether this expression requires Nxt1. We additionally find that transcripts from intron-less genes are more sensitive to loss of Nxt1 function than those from intron-containing genes and propose a mechanism in which transcript processing feeds back to increase activity of a tissue specific transcription complex.

SUBMITTER: Caporilli S

PROVIDER: S-EPMC3674997 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTranscription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize.ResultsAlthough many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm.ConclusionsBy combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code and data are available at Github ( https://github.com/timedreamer/maize_tissue-specific_GRN ).

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The RNA export factor, Nxt1, is required for tissue specific transcriptional regulation.

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