The regulation of gene expression plays a pivotal role in all aspects of biology, from the manner in which bacteria respond to their environment to the differentiation of tissues in higher eukaryotes. In the era of genomics, proteomics, and metabolomics, however, biologists are still bereft of a generally applicable method for rapid determination of the regulatory logic underlying the pattern of gene expression in a cell under a given set of conditions. This logic arises in large part from the binding of transcription factors (TFs) which can either repress or activate expression of nearby genes. The K99/R00 project proposed here aims to contribute a method, termed IPODHR, for obtaining a genome-wide snapshot of the transcriptional regulatory state of the cell, by providing the locations and identities of all transcription factors bound to the genome under physiological conditions. Understanding and quantitatively modeling the regulatory networks of bacterial cells is crucial both for the successful development of new antibiotics, and for the rational manipulation of microbial communities such as that in the human gut. IPODHR is super?cially similar to chromatin immunoprecipitation (ChIP) experiments, but instead of isolat- ing a single protein (and any DNA bound to it), IPODHR isolates all protein-DNA complexes from crosslinked lysates, using the fact that these complexes partition to the organic-aqueous interphase during phenol-chloroform extraction. High throughput sequencing is used to reveal the locations of DNA-bound TFs. The resulting sig- nal, representing overall protein occupancy throughout the genome, is then split during data processing into contributions from different TFs and other DNA binding proteins, using a computational method that is currently under development. Thus, unlike ChIP, only one experiment is required to study the entire regulatory state of the cell under a given condition, and prior knowledge of the relevant TFs is not required. At present, my ongoing research (including plans for the mentored phase of the award) is focused on completing the experimental and computational aspects of the IPODHR framework. For the experimental com- ponent, only small re?nements appear necessary to improve spatial resolution further; validation experiments and pilot applications will then be performed to con?rm the sensitivity and speci?city of the method to changing physiological conditions. The computational methods required for partitioning the IPODHR binding pro?le are also under active development, using a statistical model to assign peaks in the IPODHR density to particular factors. In the process of these development and validation experiments, follow-ups will target TF binding sites and speci?cities inferred from IPODHR data but not yet characterized in detail, further expanding our knowledge of the E. coli transcriptional regulatory network by revealing new TFs and interactions. Successful completion and application of IPODHR will provide the community with a transformative new tool to measure the transcrip- tional regulatory logic of bacteria without detailed prior knowledge of the transcription factors involved. Research planned for the independent phase will focus on the use of IPODHR, alongside other established methods in bacterial systems biology, to obtain a complete understanding of how rewiring transcriptional net- works can allow cells to adapt to novel conditions without the acquisition of new enzymatic capacities. I will focus initially on a previously discovered mutation of the termination factor Rho that improves cellular ?tness under a variety of conditions, and appears to be representative of a broad class of mutations to housekeep- ing proteins that occur in evolving bacterial populations. IPODHR will allow measurement of the changes in transcriptional logic giving rise to previously observed adaptive outputs, and thus provide insight into the ex- act mechanisms through which the perturbations under study alter TF behavior to give rise to the observed changes in phenotype. As the rho mutation in question renders cells somewhat resistant to several classes of antibiotics, it will be particularly useful to compare the mechanisms of this resistance with other known paths to antibiotic tolerance. If progress on the proposed aims is suf?ciently rapid, near the end of the grant period adaptation of IPODHR for use in bacteria other than E. coli may also begin. The massive scope of information provided by the method, and lack of any need for speci?c prior knowledge or manipulation of the target organism, mean that IPODHR has the promise to provide a huge advance in the understanding of transcriptional regulation in poorly studied microbes. These applications of IPODHR will form the backbone of an R01 proposal to be prepared during the late stages of the independent R00 phase.
The cells of organisms from bacteria to humans must activate or deactivate different genes at different times in order to survive and thrive. My research is focused on developing a method for measuring the way that bacteria regulate their genes. The information that we gather will provide us with a broader understanding of how bacteria respond to their environment, and new targets for antimicrobial drug development based on disrupting those responses.
|Freddolino, Peter L; Yang, Jamie; Momen-Roknabadi, Amir et al. (2018) Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry. Elife 7:|
|Bohn, Jennifer A; Van Etten, Jamie L; Schagat, Trista L et al. (2018) Identification of diverse target RNAs that are functionally regulated by human Pumilio proteins. Nucleic Acids Res 46:362-386|
|Zhang, Chengxin; Freddolino, Peter L; Zhang, Yang (2017) COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Res 45:W291-W299|
|Khabiri, Morteza; Freddolino, Peter L (2017) Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes. J Phys Chem B 121:5151-5161|
|Ribeiro, João V; Bernardi, Rafael C; Rudack, Till et al. (2016) QwikMD?-?Integrative Molecular Dynamics Toolkit for Novices and Experts. Sci Rep 6:26536|
|Zisis, Themistoklis; Freddolino, Peter L; Turunen, Petri et al. (2015) Interfacial Activation of Candida antarctica Lipase B: Combined Evidence from Experiment and Simulation. Biochemistry 54:5969-79|