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Research

Research interests

We are developing bioinformatic tools and resources to help understand the epigenomic regulatory mechanisms that function during early maize endosperm development. The recent completion of the maize genome has facilitated understanding the epigenetic regulation of endosperm development and the molecular mechanisms underlying gene imprinting at a genomic level. Although application of next-generation sequencing technology for epigenome and transcriptome profiling has allowed accumulation of significant amount of sequence data, bioinformatic approaches are still needed to properly analyze these large datasets. Three main objectives of the ongoing projects in our Lab are to develop computational tools and resources to: 1) Improve maize gene models using active transcription-associated histone modifications, 2) Develop algorithms to screen for core TFs and build regulatory networks using nucleosome-positioning dynamics, and 3) Identify epigenetically modified, imprinted genes at the genome level. Our work will fundamentally advance our understanding of transcriptional and epigenetic regulation, genomic imprinting, and the molecular mechanisms involved in maize endosperm development.


Project

Regulation of early endosperm development in maize

Endosperm is biologically and economically important. Endosperm provides nutrients and signals to the embryo during seed development. Endosperm is an important source of food and industrial raw materials. Additionally, cereal endosperm is used as a raw material for numerous industrial products including ethanol. We are currently collaborating with Prof. Brian Larkins and Prof. Ramin Yadegari on a NSF funded project to study the regulation of maize early endosperm development. In this project we are using Illumina high-throughput sequencing to profile the mRNA transcriptome (RNA-Seq) to identify the core transcription factors, and build the regulatory network that controls the maize endosperm development in early stages. See the UA news for the description of this project at [1].

A genome browser for maize endosperm transcriptome.
smRNA
Identification of important TFs in maize endosperm development

We first conducted a in silico search of maize annotated genes in its 5.0a version in plant transcription factor database, and identify hundreds potential TF maize genes. Then we utilized publish gene microarray expression data and our self-produced RNA-seq data to further filter the gene sets. Our efforts identified ~200 candidate genes that specifically expressed in early stages of endosperm development (A). We are currently performing experimental validation of selected candidate TFs. In the next step, to construct the regulatory network of those identified TFs, we identified the genes whose expression highly correlated with those TFs and calculated their topological structures (B). The regulatory network is finally illustrated in (C). Additionally, we are developing hidden markov model (HMM) to detect the alternative splicing genes during the development (D).


Chromatin and epigenomic landscape of the developing maize endosperm

Another research interest in our Lab is to understand the epigenomic regulatory mechanisms that function during early maize endosperm development. In collaboration with Dr. Ramin Yadegari’s Lab, We are currently producing selected histone marks and DNA methylation using Illumina sequencing in a famous maize hybrid B73 cross Mo17. Another potential direction we are interested in is to study if the genomic imprinting influence the heterosis phenomena.

What is heterosis

Heterosis, or hybrid vigor, is the increased function of any biological quality in a hybrid offspring. It is the occurrence of a genetically superior offspring from mixing the genes of its parents. Nearly all field corn (maize) grown in most developed nations exhibits heterosis. Modern corn hybrids substantially outyield conventional cultivars and respond better to fertilizer.

maizeseq
Above figure was modified from two papers by (Springer and Stupar 2007 and He et al, 2010)
Improve maize gene prediction using active histone marks