Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum's response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum's metabolism

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 sigma-factor to recognize the genomic location at which the process initiates. Although the crucial role of sigma-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 s-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 sigma-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 sigma-factors (the sigma(70) and sigma(38) regulons), confirming the competition model of sigma substitution and negative regulation by alternative s-factors. Comparison with sigma-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

Applying metabolic models for control in order to enhance algal growth and lipid production

A number of metabolic models have been developed in different algae species in the past five years. In this study, a metabolic model of C. vulgaris was applied for controlling algal growth and lipid production. This method optimized nutrient supply by characterizing algal metabolic pathways under different conditions. The approach was validated for autotrophic growth under nitrogen replete condition, in which nitrate requirement was lowered while retaining robust algal growth. Furthermore, this approach was also applied for nitrogen limited environments. Previous studies have found C. vulgaris can accumulate high lipid content in biomass, which can be hydrotreated to biodiesel, while decreasing algal growth under nitrogen limitation [1]. Our method optimized the nitrate supply to sustain algal growth while still producing fatty acids efficiently under nitrogen limited conditions. Compared with C. vulgaris cultures with complete nitrogen withdrawal, fatty acid methyl esters (FAME) increased substantially. This metabolic model-based approach will have applicability for optimizing nutrient inputs and biomanufacturing across a wide spectrum of organisms ranging from prokaryotes to eukaryotes used to produce a variety of biotechnology products in coming decades. References [1] M.T. Guarnieri, A. Nag, S.L. Smolinski, A. Darzins, M. Seibert, P.T. Pienkos, Examination of triacylglycerol biosynthetic pathways via de novo transcriptomic and proteomic analyses in an unsequenced microalga, PLoS One, 6 (2011) e25851

Optimization of carbon and energy utilization through differential translational efficiency.

Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5'-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms