KLF6 and STAT3 Co-Occupy Regulatory DNA and Functionally Synergize to Promote Axon Growth in CNS Neurons

The failure of axon regeneration in the CNS limits recovery from damage and disease. Members of the KLF family of transcription factors can exert both positive and negative effects on axon regeneration, but the underlying mechanisms are unclear. Here we show that forced expression of KLF6 promotes axon regeneration by corticospinal tract neurons in the injured spinal cord. RNA sequencing identified 454 genes whose expression changed upon forced KLF6 expression in vitro, including sub-networks that were highly enriched for functions relevant to axon extension including cytoskeleton remodeling, lipid synthesis, and bioenergetics. In addition, promoter analysis predicted a functional interaction between KLF6 and a second transcription factor, STAT3, and genome-wide footprinting using ATAC-Seq data confirmed frequent co-occupancy. Co-expression of the two factors yielded a synergistic elevation of neurite growth in vitro. These data clarify the transcriptional control of axon growth and point the way toward novel interventions to promote CNS regeneration

Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP

Strategies for increasing the applicability of biological network inference

The manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and stress resistant crop development. The construction of interaction maps promises to enhance our ability to engineer cellular behavior. Within the last 15 years, many methods have been developed to infer the structure of the gene regulatory interaction map from gene abundance snapshots provided by high-throughput experimental data. However, relatively little research has focused on using gene regulatory network models for the prediction and manipulation of cellular behavior. This dissertation examines and applies strategies to utilize the predictive power of gene network models to guide experimentation and engineering efforts. First, we developed methods to improve gene network models by integrating interaction evidence sources, in order to utilize the full predictive power of the models. Next, we explored the power of networks models to guide experimental efforts through inference and analysis of a regulatory network in the pathogenic fungus Cryptococcus neoformans. Finally, we develop a novel, network-guided algorithm to select genetic interventions for engineering transcriptional state. We apply this method to select intervention strains for improving biofuel production in a mixed glucose-xylose environment. The contributions in this dissertation provide the first thorough examination, systematic application, and quantitative evaluation of the utilization of network models for guiding cellular engineering