Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states.

BACKGROUND: At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a σ-factor to recognize the genomic location at which the process initiates. Although the crucial role of σ-factors has long been appreciated and characterized for many individual promoters, we do not yet have a genome-scale assessment of their function. RESULTS: Using multiple genome-scale measurements, we elucidated the network of σ-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 σ-factor-specific promoters corresponding to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative σ-factors (the σ70 and σ38 regulons), confirming the competition model of σ substitution and negative regulation by alternative σ-factors. Comparison with σ-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent. CONCLUSIONS: The reconstructed network reveals the regulatory complexity of the promoter architecture in prokaryotic genomes, and opens a path to the direct determination of the systems biology of their transcriptional regulatory networks.

Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis.

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms.

Genetic basis of growth adaptation of Escherichia coli after deletion of pgi, a major metabolic gene.

Bacterial survival requires adaptation to different environmental perturbations such as exposure to antibiotics, changes in temperature or oxygen levels, DNA damage, and alternative nutrient sources. During adaptation, bacteria often develop beneficial mutations that confer increased fitness in the new environment. Adaptation to the loss of a major non-essential gene product that cripples growth, however, has not been studied at the whole-genome level. We investigated the ability of Escherichia coli K-12 MG1655 to overcome the loss of phosphoglucose isomerase (pgi) by adaptively evolving ten replicates of E. coli lacking pgi for 50 days in glucose M9 minimal medium and by characterizing endpoint clones through whole-genome re-sequencing and phenotype profiling. We found that 1) the growth rates for all ten endpoint clones increased approximately 3-fold over the 50-day period; 2) two to five mutations arose during adaptation, most frequently in the NADH/NADPH transhydrogenases udhA and pntAB and in the stress-associated sigma factor rpoS; and 3) despite similar growth rates, at least three distinct endpoint phenotypes developed as defined by different rates of acetate and formate secretion. These results demonstrate that E. coli can adapt to the loss of a major metabolic gene product with only a handful of mutations and that adaptation can result in multiple, alternative phenotypes.

RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media.

Specific small deletions within the rpoC gene encoding the ß'-subunit of RNA polymerase (RNAP) are found repeatedly after adaptation of Escherichia coli K-12 MG1655 to growth in minimal media. Here we present a multiscale analysis of these mutations. At the physiological level, the mutants grow 60% faster than the parent strain and convert the carbon source 15-35% more efficiently to biomass, but grow about 30% slower than the parent strain in rich medium. At the molecular level, the kinetic parameters of the mutated RNAP were found to be altered, resulting in a 4- to 30-fold decrease in open complex longevity at an rRNA promoter and a ∼10-fold decrease in transcriptional pausing, with consequent increase in transcript elongation rate. At a genome-scale, systems biology level, gene expression changes between the parent strain and adapted RNAP mutants reveal large-scale systematic transcriptional changes that influence specific cellular processes, including strong down-regulation of motility, acid resistance, fimbria, and curlin genes. RNAP genome-binding maps reveal redistribution of RNAP that may facilitate relief of a metabolic bottleneck to growth. These findings suggest that reprogramming the kinetic parameters of RNAP through specific mutations allows regulatory adaptation for optimal growth in new environments.

The transcription unit architecture of the Escherichia coli genome.

Bacterial genomes are organized by structural and functional elements, including promoters, transcription start and termination sites, open reading frames, regulatory noncoding regions, untranslated regions and transcription units. Here, we iteratively integrate high-throughput, genome-wide measurements of RNA polymerase binding locations and mRNA transcript abundance, 5' sequences and translation into proteins to determine the organizational structure of the Escherichia coli K-12 MG1655 genome. Integration of the organizational elements provides an experimentally annotated transcription unit architecture, including alternative transcription start sites, 5' untranslated region, boundaries and open reading frames of each transcription unit. A total of 4,661 transcription units were identified, representing an increase of >530% over current knowledge. This comprehensive transcription unit architecture allows for the elucidation of condition-specific uses of alternative sigma factors at the genome scale. Furthermore, the transcription unit architecture provides a foundation on which to construct genome-scale transcriptional and translational regulatory networks.

Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli.

Broad-acting transcription factors (TFs) in bacteria form regulons. Here, we present a 4-step method to fully reconstruct the leucine-responsive protein (Lrp) regulon in Escherichia coli K-12 MG 1655 that regulates nitrogen metabolism. Step 1 is composed of obtaining high-resolution ChIP-chip data for Lrp, the RNA polymerase and expression profiles under multiple environmental conditions. We identified 138 unique and reproducible Lrp-binding regions and classified their binding state under different conditions. In the second step, the analysis of these data revealed 6 distinct regulatory modes for individual ORFs. In the third step, we used the functional assignment of the regulated ORFs to reconstruct 4 types of regulatory network motifs around the metabolites that are affected by the corresponding gene products. In the fourth step, we determined how leucine, as a signaling molecule, shifts the regulatory motifs for particular metabolites. The physiological structure that emerges shows the regulatory motifs for different amino acid fall into the traditional classification of amino acid families, thus elucidating the structure and physiological functions of the Lrp-regulon. The same procedure can be applied to other broad-acting TFs, opening the way to full bottom-up reconstruction of the transcriptional regulatory network in bacterial cells.

Genomewide identification of protein binding locations using chromatin immunoprecipitation coupled with microarray.

Interactions between cis-acting elements and proteins play a key role in transcriptional regulation of all known organisms. To better understand these interactions, researchers developed a method that couples chromatin immunoprecipitation with microarrays (also known as ChIP-chip), which is capable of providing a whole-genome map of protein-DNA interactions. This versatile and high-throughput strategy is initiated by formaldehyde-mediated cross-linking of DNA and proteins, followed by cell lysis, DNA fragmentation, and immunopurification. The immunoprecipitated DNA fragments are then purified from the proteins by reverse-cross-linking followed by amplification, labeling, and hybridization to a whole-genome tiling microarray against a reference sample. The enriched signals obtained from the microarray then are normalized by the reference sample and used to generate the whole-genome map of protein-DNA interactions. The protocol described here has been used for discovering the genomewide distribution of RNA polymerase and several transcription factors of Escherichia coli.

Genome-wide analysis of Fis binding in Escherichia coli indicates a causative role for A-/AT-tracts.

We determined the genome-wide distribution of the nucleoid-associated protein Fis in Escherichia coli using chromatin immunoprecipitation coupled with high-resolution whole genome-tiling microarrays. We identified 894 Fis-associated regions across the E. coli genome. A significant number of these binding sites were found within open reading frames (33%) and between divergently transcribed transcripts (5%). Analysis indicates that A-tracts and AT-tracts are an important signal for preferred Fis-binding sites, and that A(6)-tracts in particular constitute a high-affinity signal that dictates Fis phasing in stretches of DNA containing multiple and variably spaced A-tracts and AT-tracts. Furthermore, we find evidence for an average of two Fis-binding regions per supercoiling domain in the chromosome of exponentially growing cells. Transcriptome analysis shows that approximately 21% of genes are affected by the deletion of fis; however, the changes in magnitude are small. To address the differential Fis bindings under growth environment perturbation, ChIP-chip analysis was performed using cells grown under aerobic and anaerobic growth conditions. Interestingly, the Fis-binding regions are almost identical in aerobic and anaerobic growth conditions-indicating that the E. coli genome topology mediated by Fis is superficially identical in the two conditions. These novel results provide new insight into how Fis modulates DNA topology at a genome scale and thus advance our understanding of the architectural bases of the E. coli nucleoid.

Systems approach to refining genome annotation.

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.

Transcriptional regulation of the fad regulon genes of Escherichia coli by ArcA.

ArcA is a global transcription factor required for optimal growth of Escherichia coli during anaerobic growth. In this study, the role of ArcA on the transcriptional regulatory subnetwork of the fad regulon was investigated. Gene expression profiles of deletion mutants (Delta arcA, Delta fadR and Delta arcA/Delta fadR) indicated that (i) ArcA is a major transcription factor for the transcriptional regulation of fatty acid metabolism in the absence of oxygen, and (ii) ArcA and FadR cooperatively regulate the fad regulon under anaerobic conditions. To determine the direct interaction between ArcA and the promoters of the fad regulon genes, chromatin immunoprecipitation (ChIP) analysis was performed. ChIP analysis suggested that ArcA directly binds to the promoter regions of the fad regulon genes in vivo. An ArcA-binding motif was identified from known binding sequences and predicted putative binding sites in the promoter regions of the fad regulon genes. These results indicate that ArcA directly represses the expression of fad regulon genes during anaerobic growth.

PCR-based tandem epitope tagging system for Escherichia coli genome engineering.

Biological discovery in the postgenomic era requires a systematic and high-throughput experimental approach. To this end, a versatile PCR-based tandem epitope tagging system is described, which inserts a tandem epitope coding sequence into any desired position of the Escherichia coli chromosome. Template plasmids were constructed that carry tandem copies of the epitope encoding sequence, Flp recombinase target (FRT) sites, and antibiotic resistance genes. The linear DNA fragment, amplified from the template plasmid with extensions homologous to the end of the target gene and part of its downstream region, was transformed into E. coli K-12 MG1655 harboring the bacteriophage gamma Red recombination system. The antibiotic resistance gene was then removed from the inserted heterologous PCR fragment using Flp recombinase. This epitope tagging system was applied to global transcription factors of E. coli to obtain proteins fused with tandem c-myc epitope tags. The tandem myc epitope-fused transcription factors were successfully detected by Western blot analysis and chromatin immunoprecipitation with increased detection sensitivity and higher yield. Higher copy numbers of the epitope molecule allowed the use of more stringent experimental conditions to increase the signal-to-noise ratio in subsequent experimental applications. Furthermore, judging from the measurement of gene expression using reverse transcription PCR (RT-PCR), the epitope-fused transcription factors retained their normal function for gene regulation in vivo.

In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

The development and validation of new methods to help direct rational strain design for metabolite overproduction remains an important problem in metabolic engineering. Here we show that computationally predicted E. coli strain designs, calculated from a genome-scale metabolic model, can lead to successful production strains and that adaptive evolution of the engineered strains can lead to improved production capabilities. Three strain designs for lactate production were implemented yielding a total of 11 evolved production strains that were used to demonstrate the utility of this integrated approach. Strains grown on 2 g/L glucose at 37 degrees C showed lactate titers ranging from 0.87 to 1.75 g/L and secretion rates that were directly coupled to growth rates.

Integrating high-throughput and computational data elucidates bacterial networks.

The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.