ChIP-exo interrogation of Crp, DNA, and RNAP holoenzyme interactions

Numerous in vitro studies have yielded a refined picture of the structural and molecular associations between Cyclic-AMP receptor protein (Crp), the DNA motif, and RNA polymerase (RNAP) holoenzyme. In this study, high-resolution ChIP-exonuclease (ChIP-exo) was applied to study Crp binding in vivo and at genome-scale. Surprisingly, Crp was found to provide little to no protection of the DNA motif under activating conditions. Instead, Crp demonstrated binding patterns that closely resembled those generated by σ70. The binding patterns of both Crp and σ70 are indicative of RNAP holoenzyme DNA footprinting profiles associated with stages during transcription initiation that occur post-recruitment. This is marked by a pronounced advancement of the template strand footprint profile to the +20 position relative to the transcription start site and a multimodal distribution on the nontemplate strand. This trend was also observed in the familial transcription factor, Fnr, but full protection of the motif was seen in the repressor ArcA. Given the time-scale of ChIP studies and that the rate-limiting step in transcription initiation is typically post recruitment, we propose a hypothesis where Crp is absent from the DNA motif but remains associated with RNAP holoenzyme post-recruitment during transcription initiation. The release of Crp from the DNA motif may be a result of energetic changes that occur as RNAP holoenzyme traverses the various stable intermediates towards elongation complex formation

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data

Minimal metabolic pathway structure is consistent with associated biomolecular interactions

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated

The PurR regulon in Escherichia coli K-12 MG1655

The PurR transcription factor plays a critical role in transcriptional regulation of purine metabolism in enterobacteria. Here, we elucidate the role of PurR under exogenous adenine stimulation at the genome-scale using high-resolution chromatin immunoprecipitation (ChIP)–chip and gene expression data obtained under in vivo conditions. Analysis of microarray data revealed that adenine stimulation led to changes in transcript level of about 10% of Escherichia coli genes, including the purine biosynthesis pathway. The E. coli strain lacking the purR gene showed that a total of 56 genes are affected by the deletion. From the ChIP–chip analysis, we determined that over 73% of genes directly regulated by PurR were enriched in the biosynthesis, utilization and transport of purine and pyrimidine nucleotides, and 20% of them were functionally unknown. Compared to the functional diversity of the regulon of the other general transcription factors in E. coli, the functions and size of the PurR regulon are limited

Elucidating mechanisms of transcriptional regulation at the genome-scale

Throughout the course of evolution, almost all organisms have generated complex, hierarchical, and robust regulatory systems. One major component of these biological regulatory systems is the transcriptional activation or repression of gene expression. This regulation is carried out simultaneously across a genome by thousands of biological components at thousands of individual promoters. The sum total of all of the regulatory events and their interconnections or overlaps is commonly referred to as the transcriptional regulatory network. The focus of this thesis is to determine the mechanisms or guiding principles behind these transcriptional regulatory networks and to provide a basis upon which predictive mathematical models of these networks can be built. In the first section, a reconstruction of the full transcriptional regulatory network for a model organism is presented along with the OME software framework developed to handle the full complexity of genome-scale datasets and models. In the second section, the mechanisms of individual regulatory events are elucidated in a massively parallel fashion using ChIP- exonuclease and the OME framework. This leads to fundamental insights into the nature of transcriptional initiation complexes for canonical regulators. Finally, in the third section, an effort is undertaken to determine systems level mechanisms which dictate the coordinate regulation of hundreds of simultaneous regulatory events in response to major physiological and metabolic perturbations. Here we show that the two principal dimensions of a metabolic system, growth and the production of energy, drive not only the organization of the metabolic network, but also the organization of the transcriptional regulatory networ

Whole transcriptome analysis of the silicon response of the diatom Thalassiosira pseudonana

BACKGROUND: Silicon plays important biological roles, but the mechanisms of cellular responses to silicon are poorly understood. We report the first analysis of cell cycle arrest and recovery from silicon starvation in the diatom Thalassiosira pseudonana using whole genome microarrays. RESULTS: Three known responses to silicon were examined, 1) silicified cell wall synthesis, 2) recovery from silicon starvation, and 3) co-regulation with silicon transporter (SIT) genes. In terms of diatom cell wall formation, thus far only cell surface proteins and proteins tightly associated with silica have been characterized. Our analysis has identified new genes potentially involved in silica formation, and other genes potentially involved in signaling, trafficking, protein degradation, glycosylation and transport, which provides a larger-scale picture of the processes involved. During silicon starvation, an overrepresentation of transcription and translation related genes were up-regulated, indicating that T. pseudonana is poised to rapidly recover from silicon starvation and resume cell cycle progression upon silicon replenishment. This is in contrast to other types of limitation, and provides the first molecular data explaining the well-established environmental response of diatoms to grow as blooms and to out-compete other classes of microalgae for growth. Comparison of our data with a previous diatom cell cycle analysis indicates that assignment of the cell cycle specific stage of particular cyclins and cyclin dependent kinases should be re-evaluated. Finally, genes co-varying in expression with the SITs enabled identification of a new class of diatom-specific proteins containing a unique domain, and a putative silicon efflux protein. CONCLUSIONS: Analysis of the T. pseudonana microarray data has provided a wealth of new genes to investigate previously uncharacterized cellular phenomenon related to silicon metabolism, silicon’s interaction with cellular components, and environmental responses to silicon