A Gapless, Unambiguous Genome Sequence of the Enterohemorrhagic Escherichia coli O157:H7 Strain EDL933.

Escherichia coli EDL933 is the prototypic strain for enterohemorrhagic E. coli serotype O157:H7, associated with deadly food-borne outbreaks. Because the publicly available sequence of the EDL933 genome has gaps and >6,000 ambiguous base calls, we here present an updated high-quality, unambiguous genome sequence with no assembly gaps

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

Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

BACKGROUND: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H(2)/CO(2), and more importantly on synthesis gas (H(2)/CO/CO(2)) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. RESULTS: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. CONCLUSIONS: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels

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

An integrated workflow for the multi-omic characterization of microorganisms

In this dissertation, I provide a generalized framework for the in depth molecular characterization of microorganisms using multi-omic data integration. Next- generation sequencing is rapidly becoming a staple in biological research. However, as data generation becomes more routine, greater attention must be placed on the analytics needed to extract useful information from these datasets. Recent work using multi-omic characterization approaches have revealed that microbial genomes and their organizational features are far more complex than previously thought. Here, I present a multi-omic data integration strategy that updates and expands upon previously implemented workflows that follow four principles to study microbial transcription, translation, and regulation : (1) data generation, (2) data processing, (3) data integration, (4) data analysis. At the core of this integrative workflow is the a complete and accurate reference genome assembly. Chapter 2 discusses updated sequences and gene annotation for Thermotoga maritima and Escherichia coli O157:H7 EDL933 using next-generation sequencing. With the genome sequence revealed, expanded annotation of cellular features is achieved quantitatively using a blend of genome-scale experimental methods and complimentary bioinformatics approaches where the genome serves as a normalizing factor. In Chapter 3 this multifaceted approach is used to elucidate the genome organization of T. maritima and revealed novel insights into its hyperthermophilic lifestyle. The detailed characterization of the T. maritima genome organization was applied in Chapter 4 where the genotype-to-phenotype relationship associated with laboratory evolved cultures were revealed. A genomic region excluded from the original genome sequence was found to be modulated in response to the applied selective pressure, underscoring the importance of the accuracy of the genome sequence. Furthermore, recently developed next-generation approaches were implemented in the multi-omic workflow to provided in vivo, genome-scale data on transcriptional regulation (ChIP-exo, Chapter 5) and protein translation (ribosome profiling, Chapter 6). These assays provide stark improvements compared to previously implemented methodologies with respect to their resolution, signal-to- noise, and cost. They also reveal detailed molecular interactions that previously could not be discerned at genome-scale. Collectively, the workflow utilized here will enable researchers to rapidly and cost-effectively characterize microbial systems beyond the one-dimensional genome annotation and towards complete elucidation of the multidimensional genome organizatio