Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Abstract Background Transcription 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. Results Although 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. Conclusions By 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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Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Additional file 12: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Degree centrality (Number of targets) of top 1 million edges for TFs in (A) Leaf GRN, (B) Root GRN, (C) SAM GRN and (D) Seed GRM. Red lines showing TF with degree centrality > 2000. (PDF 338 kb

Additional file 8: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Predicted TF target overlap with ChIP-Seq confirmed binding genes for KN1, FEA4 and O2. The leaf, root, SAM and seed refer to our tissue GRNs. “Protein” and “RNA” refer to protein GRN and RNA GRN from Walley et al. (2016) dataset. “Large network” used top 10 million edges in KN1, FEA4 and O2 networks. “Medium network” used top 1 million edges, while “Small network” used top 100,000 edges. “Atlas GRN medium” used top 1 million edges from Walley et al. (2016) dataset while “Atlas GRN small” used top 100,000 edges. (XLSX 13 kb

Additional file 7: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

GO enrichment analysis for KN1, FEA4 and O2 targets in four tissue GRNs. (XLSX 19 kb

Additional file 3: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Evaluation of tissue GRNs generated by MRNET and CLR. (XLSX 9 kb

Additional file 2: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

GO enrichment analysis for tissue-specific genes in four tissue GRNs. (XLSX 23 kb

Additional file 6: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

GO enrichment analysis for 1657 conserved targets in four tissue GRNs. (XLSX 22 kb

Additional file 5: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

The 353 TFs and 1657 targets included in the 2679 edges shared by four tissue GRNs. (XLSX 70 kb