The University of Illinois at Urbana-Champaign is awarded a grant to advance the understanding of the evolution of complex traits, such as the development of multi-cellular body plans or an organism's social behavior. The PI will use the analysis of brain plasticity and social behavior as test-bed questions to build and test quantitative genomic models for the evolution of complex traits. Brain plasticity refers to the brain's ability to reorganize itself by forming new neural connections as a result of experience. The project will test the hypothesis that genetic pathways that mediate brain plasticity are enriched in conserved gene regulatory modules in brain tissues across species, by developing novel analytical models and computational tools for modeling the evolution of gene regulatory modules. The first model is an evolutionary model for genetic modules, which can be applied to identify conserved as well as species-specific genetic modules using and gene expression data in multiple species and phylogenetic distances. The second model can be applied to analyze the transcription networks implemented in related species with a conserved phenotype. The regulatory relationships between an orthologous set of TFs and target genes will be simultaneously modeled and identified in all the species under consideration. Both sequence data and gene expression data, when available, will be modeled. This project will deliver two software tools for integrated comparative analysis of genome and transcriptome data. by These software tools will be hosted on a dedicated server (http://sysbio.bioen.uiuc.edu/grn.htm) and made available to the entire research community through user-friendly web applications. The project will include the participation of undergraduate, graduate, and postdoctoral students, and the software will be used in a hands-on course taught by the investigator at a bioinformatics camp for young women.
This project produced a set of computational models. We developed an evolutionary model of transcription networks (TN). This is one of the first a quantitative evolutionary models of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF) and its target genes in all species, and thus identify TN re-wiring events. We implemented and tested a program on identification of conserved and species-specific gene expression modules. This program enables joint clustering of multi-species gene expression data but does not require the orthologous genes to be grouped into orthologous clusters. We developed a computational method to change the reference human genome into an individual genome, taking into account single nucleotide polymorphisms (SNPs), insertions and deletions, copy number variation, and chromosomal rearrangements. This method fulfills a critical step in using personal genome sequence and ChIP experiments to studying variation in transcription factor (TF) binding, chromatin structure and gene expression. This tool is a beginning of studying the association of TN variation to phenotypic variation. This project produced a few bioinformatics software tools. We wrote a software tool (EvoRgNet) for model the evolution of transcription networks. We made this software open source and fully documented. We provided several sample datasets for users to make test run. See http://systemsbio.ucsd.edu/EvoRgNet. We wrote a software tool (SCSC: cross-species soft clustering) for identification of conserved and species-specific gene expression modules. See: http://systemsbio.ucsd.edu/SCSC/tools/SCSC.html We wrote perEditor as into a software tool and provided it with full documentation and open source. See: http://systemsbio.ucsd.edu/perEditor/. This project generated biological insights. We showed that behavioral differences were associated with large-scale changes in gene expression. We analyzed how a transcription factor (TF), ultraspiracle (usp; the insect homolog of the Retinoid X Receptor), working in complex transcriptional networks, can regulate behavioral plasticity and associated changes in gene expression. This project trained postdocs, graduate and undergraduate students on computational analysis of genomic data and modeling transcription networks. Two students successfully defended their Ph.D. theses.
