Our laboratory pursues a wide variety of research applying innovative high throughput sequencing, bioinformatics, and biophysical simulations to understand and model regulatory networks. Some of our current areas of interest include:

Global mapping of bacterial transcriptional regulatory states

Using a combination of IPODHR (a recently developed method for genome-wide profiling of protein-DNA interactions), ChIP-seq, RNA-seq, and bioinformatic analysis, we are elucidating the complete regulatory logic underlying starvation responses, persistence, and responses to antibiotic challenge in E. coli. Future applications of similar methods to less well-studied bacteria will enable the rapid mapping of previously uncharacterized regulatory networks with a compact set of experiments.

Group members: Tom Goss, Grace KronerRebecca Hurto

Biophysics of transcriptional regulation

One of the most crucial stages of transcriptional regulation in all organisms is at the level of interactions of DNA, transcription factors, and RNA polymerase. Complexities abound, as each DNA-binding protein will have a particular spectrum of affinities for different DNA sequences, plus its own effects on the local structure of bound DNA that will in turn influence the interactions of proteins with adjacent sections of the genome. We are applying a combination of molecular dynamics simulations of transcription factor-DNA interactions, reanalysis of existing data sets using machine learning, and complimentary in vitro experiments, to map out the effects of transcription factor binding on DNA structure, and the effects of changes to local structure on the sequence affinity landscape of different transcription factors. This will allow us to generate physics-based models for the distribution of transcription factors on DNA under realistic conditions and in the face of changes to the cells' environment.

Group members: Morteza Khabiri, Taeho Jo

Structure-based functional annotation of bacterial genomes

Recent advances in high-throughput sequencing technology have lead to an explosion in the number of genomic sequences available, but our ability to provide high quality annotations lags far behind. The gap is particularly apparent in the field of microbiology, where thousands of taxa are potentially influential or useful in human health and disease, environmental settings, and synthetic biology applications. Our ability to take advantage of our growing body of sequence knowledge thus hinges primarily on computational annotations of newly sequenced genomes. Almost all currently available annotations are based on sequence-homology transfer, which is accurate for highly homologous sequences but drops in accuracy as sequence identities fall below 50%. Unfortunately, the vast majority of known protein sequences have less than 50% identity to any protein with high quality experimental annotations, and thus a different approach is called for. In collaboration with the Yang Zhang laboratory here at Michigan, we are developing and applying pipelines for whole-proteome structural prediction and functional annotation for bacterial genomes, and have already demonstrated performance competitive with, and in some cases superior to, existing sequence-based methods.

Group members: Mehdi Rahimpour

Gene regulation in metazoan energy usage and obesity

Obesity is a major risk factor in a wide variety of diseases including diabetes, cancer, and heart disease. We are working to understand the information flow governing how animals regulate both energy usage in the body, and subsequent changes to behavior as a result of energy availability, at the cellular level. Our interest in energy usage is focused on the ribonuclease Nocturnin, a gene known to regulate fat metabolism in both mice and humans through unknown mechanisms. We are collaborating with the laboratories of Dr. Ray Trievel and Dr. Aaron Goldstrohm to determine both the mechanism and targets of Nocturnin. At a behavioral level, in collaboration with Dr. Monica Dus, we are mapping the effects of high sugar diets on gene regulation in Drosophila neurons, and have identified major rearrangements of neuronal regulatory states in response to energy surplus.

Group members: Morteza Khabiri, Rucheng Diao

Inference of protein-RNA interactions from high throughput data sets

We are developing new computational approaches to infer key post-transcriptional regulatory interactions from low-throughput (e.g., PAR-CliP) and high throughput (e.g., GPARCLIP) methods for identifying protein-RNA interactions. We are also adapting the experimental and computational tools that we have developed from our work on IPODHR to perform and analyze additional experiments allowing high-throughput identification of RNA-protein interactions in E. coli under physiological conditions.

Group members: Michael Wolfe