Characterizing Gene Networks and RNA-Mediated Gene Regulation in Maize

Controlling spatial-temporal gene expression patterns is a fundamental task for maize growth and development. With the emergence of massively parallel sequencing, genome-wide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. In one project, using publicly available data, a Gene Co-expression Network (GCN) was constructed and used for gene function prediction, candidate gene selection and improving understanding of regulatory pathways. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and ranked aggregation strategy on network performance using libraries from 1266 maize samples was conducted. Three normalization methods (VST, CPM, RPKM) and ten inference methods, including six correlation and four mutual information (MI) methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than MI methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks. In another project, a maize mutant, transgene reactivated 9-1 (tgr9-1) in the transcriptional gene silencing (TGS) pathway, was cloned. The B-A translocation lines were used to map tgr9-1 on chromosome 3 and this result was consistent with molecular markers. To further locate tgr9-1, next-generation sequencing (NGS) combined with bulk segregant analysis was applied to the tgr9-1 mapping population. Using coexpression analysis, our result indicates a maize dicer-like3a (Zmdcl3a) gene is a high-confidence candidate gene for tgr9. Zmdcl3a is involved in the RNA-directed DNA methylation (RdDM) pathway. This pathway is driven by two plant-specific DNA-dependent RNA polymerases, Polymerase IV (Pol IV) and Polymerase V (Pol V). Several kinds of non-coding RNAs are involved, including long single-stranded RNAs, double-stranded RNAs, and small interfering RNAs. The identification of tgr9-1 uncovered the role of non-coding RNAs in TGS and revealed the diversity of TGS pathways in maize. One primary focus of gene regulation study is by studying transcription factors (TFs). 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. Little is known about tissue-specific gene regulation through TFs in maize. In this project, a network approach was applied to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, shoot apical meristem and seed) in maize. We used GENIE3 machine-learning algorithm combined with the large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. 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. The 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.